chore: save local changes

backend/app/main.py

@@ -3,10 +3,11 @@ from contextlib import asynccontextmanager
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 
-from .routers import attributes, transformation, expert_transformation, deduplication
+from .routers import attributes, transformation, expert_transformation, deduplication, patent_search
 from .services.llm_service import ollama_provider
 from .services.embedding_service import embedding_service
 from .services.llm_deduplication_service import llm_deduplication_service
+from .services.patent_search_service import patent_search_service
 
 
 @asynccontextmanager
@@ -15,6 +16,7 @@ async def lifespan(app: FastAPI):
     await ollama_provider.close()
     await embedding_service.close()
     await llm_deduplication_service.close()
+    await patent_search_service.close()
 
 
 app = FastAPI(
@@ -36,6 +38,7 @@ app.include_router(attributes.router)
 app.include_router(transformation.router)
 app.include_router(expert_transformation.router)
 app.include_router(deduplication.router)
+app.include_router(patent_search.router)
 
 
 @app.get("/")

backend/app/models/schemas.py

@@ -1,7 +1,10 @@
 from pydantic import BaseModel
-from typing import Optional, List, Dict
+from typing import Optional, List, Dict, Literal
 from enum import Enum
 
+# Language type for prompts
+LanguageType = Literal["zh", "en"]
+
 
 class AttributeNode(BaseModel):
     name: str
@@ -47,16 +50,19 @@ class CausalChain(BaseModel):
 
 
 class StreamAnalyzeRequest(BaseModel):
-    """多步驟分析請求（更新為支持動態類別）"""
+    """Multi-step analysis request (updated to support dynamic categories)"""
     query: str
     model: Optional[str] = None
     temperature: Optional[float] = 0.7
-    chain_count: int = 5  # 用戶可設定要生成多少條因果鏈
+    chain_count: int = 5  # User can set how many causal chains to generate
 
-    # 新增：動態類別支持
-    category_mode: Optional[str] = "dynamic_auto"  # CategoryMode enum 值
+    # Dynamic category support
+    category_mode: Optional[str] = "dynamic_auto"  # CategoryMode enum value
     custom_categories: Optional[List[str]] = None
-    suggested_category_count: int = 3  # 建議 LLM 生成的類別數量
+    suggested_category_count: int = 3  # Suggest LLM to generate this many categories
+
+    # Language setting
+    lang: LanguageType = "zh"
 
 
 class StreamAnalyzeResponse(BaseModel):
@@ -136,13 +142,14 @@ class DAGRelationship(BaseModel):
 # ===== Transformation Agent schemas =====
 
 class TransformationRequest(BaseModel):
-    """Transformation Agent 請求"""
-    query: str                        # 原始查詢 (e.g., "腳踏車")
-    category: str                     # 類別名稱 (e.g., "功能")
-    attributes: List[str]             # 該類別的屬性列表
+    """Transformation Agent request"""
+    query: str                        # Original query (e.g., "bicycle")
+    category: str                     # Category name (e.g., "Functions")
+    attributes: List[str]             # Attribute list for this category
     model: Optional[str] = None
     temperature: Optional[float] = 0.7
-    keyword_count: int = 3            # 要生成的新關鍵字數量
+    keyword_count: int = 3            # Number of new keywords to generate
+    lang: LanguageType = "zh"         # Language for prompts
 
 
 class TransformationDescription(BaseModel):
@@ -215,24 +222,27 @@ class ExpertSource(str, Enum):
 
 
 class ExpertTransformationRequest(BaseModel):
-    """Expert Transformation Agent 請求"""
+    """Expert Transformation Agent request"""
     query: str
     category: str
     attributes: List[str]
 
     # Expert parameters
-    expert_count: int = 3                        # 專家數量 (2-8)
-    keywords_per_expert: int = 1                 # 每個專家為每個屬性生成幾個關鍵字 (1-3)
-    custom_experts: Optional[List[str]] = None   # 用戶指定專家 ["藥師", "工程師"]
+    expert_count: int = 3                        # Number of experts (2-8)
+    keywords_per_expert: int = 1                 # Keywords per expert per attribute (1-3)
+    custom_experts: Optional[List[str]] = None   # User-specified experts
 
     # Expert source parameters
-    expert_source: ExpertSource = ExpertSource.LLM  # 專家來源
-    expert_language: str = "en"                     # 外部來源的語言 (目前只有英文資料)
+    expert_source: ExpertSource = ExpertSource.LLM  # Expert source
+    expert_language: str = "en"                     # Language for external sources
 
     # LLM parameters
     model: Optional[str] = None
     temperature: Optional[float] = 0.7
 
+    # Prompt language
+    lang: LanguageType = "zh"
+
 
 # ===== Deduplication Agent schemas =====
 
@@ -243,11 +253,12 @@ class DeduplicationMethod(str, Enum):
 
 
 class DeduplicationRequest(BaseModel):
-    """去重請求"""
+    """Deduplication request"""
     descriptions: List[ExpertTransformationDescription]
-    method: DeduplicationMethod = DeduplicationMethod.EMBEDDING  # 去重方法
-    similarity_threshold: float = 0.85  # 餘弦相似度閾值 (0.0-1.0)，僅 Embedding 使用
-    model: Optional[str] = None  # Embedding/LLM 模型
+    method: DeduplicationMethod = DeduplicationMethod.EMBEDDING  # Deduplication method
+    similarity_threshold: float = 0.85  # Cosine similarity threshold (0.0-1.0), only for Embedding
+    model: Optional[str] = None  # Embedding/LLM model
+    lang: LanguageType = "zh"  # Prompt language (for LLM method)
 
 
 class DescriptionGroup(BaseModel):

backend/app/prompts/attribute_prompt.py

@@ -1,20 +1,36 @@
 from typing import List, Optional, Dict
 import json
-
-DEFAULT_CATEGORIES = ["材料", "功能", "用途", "使用族群", "特性"]
-
-CATEGORY_DESCRIPTIONS = {
-    "材料": "物件由什麼材料組成",
-    "功能": "物件能做什麼",
-    "用途": "物件在什麼場景使用",
-    "使用族群": "誰會使用這個物件",
-    "特性": "物件有什麼特徵",
-}
+from .language_config import (
+    LanguageType,
+    DEFAULT_CATEGORIES,
+    CATEGORY_DESCRIPTIONS,
+)
 
 
-def get_attribute_prompt(query: str, categories: Optional[List[str]] = None) -> str:
+def get_default_categories(lang: LanguageType = "zh") -> List[str]:
+    return DEFAULT_CATEGORIES.get(lang, DEFAULT_CATEGORIES["zh"])
+
+
+def get_category_descriptions(lang: LanguageType = "zh") -> Dict[str, str]:
+    return CATEGORY_DESCRIPTIONS.get(lang, CATEGORY_DESCRIPTIONS["zh"])
+
+
+def get_attribute_prompt(
+    query: str,
+    categories: Optional[List[str]] = None,
+    lang: LanguageType = "zh"
+) -> str:
     """Generate prompt with causal chain structure."""
+    if lang == "en":
+        prompt = f"""Analyze the attributes of "{query}" in a causal chain format: Materials→Functions→Usages→User Groups.
 
+List 3-5 types of materials, each extending into a complete causal chain.
+
+JSON format:
+{{"name": "{query}", "children": [{{"name": "Material Name", "category": "Materials", "children": [{{"name": "Function Name", "category": "Functions", "children": [{{"name": "Usage Name", "category": "Usages", "children": [{{"name": "User Group Name", "category": "User Groups"}}]}}]}}]}}]}}
+
+Return JSON only."""
+    else:
         prompt = f"""分析「{query}」的屬性，以因果鏈方式呈現：材料→功能→用途→使用族群。
 
 請列出 3-5 種材料，每種材料延伸出完整因果鏈。
@@ -27,8 +43,17 @@ JSON 格式：
     return prompt
 
 
-def get_step1_attributes_prompt(query: str) -> str:
-    """Step 1: 生成各類別的屬性列表（平行結構）"""
+def get_step1_attributes_prompt(query: str, lang: LanguageType = "zh") -> str:
+    """Step 1: Generate attribute list for each category (parallel structure)"""
+    if lang == "en":
+        return f"""/no_think
+Analyze "{query}" and list attributes for the following four categories. List 3-5 common attributes for each category.
+
+Return JSON only, in the following format:
+{{"materials": ["material1", "material2", "material3"], "functions": ["function1", "function2", "function3"], "usages": ["usage1", "usage2", "usage3"], "users": ["user group1", "user group2", "user group3"]}}
+
+Object: {query}"""
+    else:
         return f"""/no_think
 分析「{query}」，列出以下四個類別的屬性。每個類別列出 3-5 個常見屬性。
 
@@ -45,10 +70,38 @@ def get_step2_causal_chain_prompt(
     usages: List[str],
     users: List[str],
     existing_chains: List[dict],
-    chain_index: int
+    chain_index: int,
+    lang: LanguageType = "zh"
 ) -> str:
-    """Step 2: 生成單條因果鏈"""
+    """Step 2: Generate a single causal chain"""
     existing_chains_text = ""
+
+    if lang == "en":
+        if existing_chains:
+            chains_list = [
+                f"- {c['material']} → {c['function']} → {c['usage']} → {c['user']}"
+                for c in existing_chains
+            ]
+            existing_chains_text = f"""
+[Already generated causal chains, do not repeat]
+{chr(10).join(chains_list)}
+"""
+        return f"""/no_think
+Generate causal chain #{chain_index} for "{query}".
+
+[Available Materials] {', '.join(materials)}
+[Available Functions] {', '.join(functions)}
+[Available Usages] {', '.join(usages)}
+[Available User Groups] {', '.join(users)}
+{existing_chains_text}
+[Rules]
+1. Select one attribute from each category to form a logical causal chain
+2. The causal relationship must be logical (materials determine functions, functions determine usages, usages determine user groups)
+3. Do not repeat existing causal chains
+
+Return JSON only:
+{{"material": "selected material", "function": "selected function", "usage": "selected usage", "user": "selected user group"}}"""
+    else:
         if existing_chains:
             chains_list = [
                 f"- {c['material']} → {c['function']} → {c['usage']} → {c['user']}"
@@ -58,7 +111,6 @@ def get_step2_causal_chain_prompt(
 【已生成的因果鏈，請勿重複】
 {chr(10).join(chains_list)}
 """
-
         return f"""/no_think
 為「{query}」生成第 {chain_index} 條因果鏈。
 
@@ -76,18 +128,51 @@ def get_step2_causal_chain_prompt(
 {{"material": "選擇的材料", "function": "選擇的功能", "usage": "選擇的用途", "user": "選擇的族群"}}"""
 
 
-def get_flat_attribute_prompt(query: str, categories: Optional[List[str]] = None) -> str:
+def get_flat_attribute_prompt(
+    query: str,
+    categories: Optional[List[str]] = None,
+    lang: LanguageType = "zh"
+) -> str:
     """Generate prompt with flat/parallel categories (original design)."""
-    cats = categories if categories else DEFAULT_CATEGORIES
+    cats = categories if categories else get_default_categories(lang)
+    cat_descs = get_category_descriptions(lang)
 
     # Build category list
     category_lines = []
     for cat in cats:
-        desc = CATEGORY_DESCRIPTIONS.get(cat, f"{cat}的相關屬性")
-        category_lines.append(f"- {cat}：{desc}")
+        desc = cat_descs.get(cat, f"Related attributes of {cat}" if lang == "en" else f"{cat}的相關屬性")
+        category_lines.append(f"- {cat}: {desc}")
 
     categories_text = "\n".join(category_lines)
 
+    if lang == "en":
+        prompt = f"""/no_think
+You are an object attribute analysis expert. Please break down the user's input object into the following attribute categories.
+
+[Required Categories]
+{categories_text}
+
+[Important] The return format must be valid JSON, and each node must have a "name" field:
+
+```json
+{{
+  "name": "Object Name",
+  "children": [
+    {{
+      "name": "Category Name",
+      "children": [
+        {{"name": "Attribute 1"}},
+        {{"name": "Attribute 2"}}
+      ]
+    }}
+  ]
+}}
+```
+
+Return JSON only, no other text.
+
+User input: {query}"""
+    else:
         prompt = f"""/no_think
 你是一個物件屬性分析專家。請將用戶輸入的物件拆解成以下屬性類別。
 
@@ -123,9 +208,37 @@ def get_flat_attribute_prompt(query: str, categories: Optional[List[str]] = None
 def get_step0_category_analysis_prompt(
     query: str,
     suggested_count: int = 3,
-    exclude_categories: List[str] | None = None
+    exclude_categories: List[str] | None = None,
+    lang: LanguageType = "zh"
 ) -> str:
-    """Step 0: LLM 分析建議類別"""
+    """Step 0: LLM analyzes and suggests categories"""
+
+    if lang == "en":
+        exclude_text = ""
+        if exclude_categories:
+            exclude_text = f"\n[Forbidden Categories] {', '.join(exclude_categories)} (These are already fixed categories, do not suggest duplicates)\n"
+
+        return f"""/no_think
+Analyze "{query}" and suggest {suggested_count} most suitable attribute categories to describe it.
+
+[Common Category References] Characteristics, Shape, Color, Size, Brand, Price Range, Weight, Style, Occasion, Season, Technical Specifications
+{exclude_text}
+[Important]
+1. Choose categories that best describe the essence of this object
+2. Categories should have logical relationships
+3. Do not choose overly abstract or duplicate categories
+4. Must suggest creative categories different from the reference list
+
+Return JSON only:
+{{
+  "categories": [
+    {{"name": "Category1", "description": "Description1", "order": 0}},
+    {{"name": "Category2", "description": "Description2", "order": 1}}
+  ]
+}}
+
+Object: {query}"""
+    else:
         exclude_text = ""
         if exclude_categories:
             exclude_text = f"\n【禁止使用的類別】{', '.join(exclude_categories)}（這些已經是固定類別，不要重複建議）\n"
@@ -154,20 +267,34 @@ def get_step0_category_analysis_prompt(
 
 def get_step1_dynamic_attributes_prompt(
     query: str,
-    categories: List  # List[CategoryDefinition]
+    categories: List,  # List[CategoryDefinition]
+    lang: LanguageType = "zh"
 ) -> str:
-    """動態 Step 1 - 根據類別列表生成屬性"""
-    # 按 order 排序並構建描述
+    """Dynamic Step 1 - Generate attributes based on category list"""
+    # Sort by order and build description
     sorted_cats = sorted(categories, key=lambda x: x.order if hasattr(x, 'order') else x.get('order', 0))
 
     category_desc = "\n".join([
-        f"- {cat.name if hasattr(cat, 'name') else cat['name']}: {cat.description if hasattr(cat, 'description') else cat.get('description', '相關屬性')}"
+        f"- {cat.name if hasattr(cat, 'name') else cat['name']}: {cat.description if hasattr(cat, 'description') else cat.get('description', 'Related attributes' if lang == 'en' else '相關屬性')}"
         for cat in sorted_cats
     ])
 
     category_keys = [cat.name if hasattr(cat, 'name') else cat['name'] for cat in sorted_cats]
-    json_template = {cat: ["屬性1", "屬性2", "屬性3"] for cat in category_keys}
 
+    if lang == "en":
+        json_template = {cat: ["attribute1", "attribute2", "attribute3"] for cat in category_keys}
+        return f"""/no_think
+Analyze "{query}" and list attributes for the following categories. List 3-5 common attributes for each category.
+
+[Category List]
+{category_desc}
+
+Return JSON only:
+{json.dumps(json_template, ensure_ascii=False, indent=2)}
+
+Object: {query}"""
+    else:
+        json_template = {cat: ["屬性1", "屬性2", "屬性3"] for cat in category_keys}
         return f"""/no_think
 分析「{query}」，列出以下類別的屬性。每個類別列出 3-5 個常見屬性。
 
@@ -185,17 +312,46 @@ def get_step2_dynamic_causal_chain_prompt(
     categories: List,  # List[CategoryDefinition]
     attributes_by_category: Dict[str, List[str]],
     existing_chains: List[Dict[str, str]],
-    chain_index: int
+    chain_index: int,
+    lang: LanguageType = "zh"
 ) -> str:
-    """動態 Step 2 - 生成動態類別的因果鏈"""
+    """Dynamic Step 2 - Generate causal chains for dynamic categories"""
     sorted_cats = sorted(categories, key=lambda x: x.order if hasattr(x, 'order') else x.get('order', 0))
 
-    # 構建可選屬性
+    # Build available attributes
     available_attrs = "\n".join([
-        f"【{cat.name if hasattr(cat, 'name') else cat['name']}】{', '.join(attributes_by_category.get(cat.name if hasattr(cat, 'name') else cat['name'], []))}"
+        f"[{cat.name if hasattr(cat, 'name') else cat['name']}] {', '.join(attributes_by_category.get(cat.name if hasattr(cat, 'name') else cat['name'], []))}"
         for cat in sorted_cats
     ])
 
+    if lang == "en":
+        # Already generated causal chains
+        existing_text = ""
+        if existing_chains:
+            chains_list = [
+                " → ".join([chain.get(cat.name if hasattr(cat, 'name') else cat['name'], '?') for cat in sorted_cats])
+                for chain in existing_chains
+            ]
+            existing_text = "\n[Already generated, do not repeat]\n" + "\n".join([f"- {c}" for c in chains_list])
+
+        # JSON template
+        json_template = {cat.name if hasattr(cat, 'name') else cat['name']: f"selected {cat.name if hasattr(cat, 'name') else cat['name']}" for cat in sorted_cats}
+
+        return f"""/no_think
+Generate causal chain #{chain_index} for "{query}".
+
+[Available Attributes]
+{available_attrs}
+{existing_text}
+
+[Rules]
+1. Select one attribute from each category
+2. Causal relationships must be logical
+3. Do not repeat
+
+Return JSON only:
+{json.dumps(json_template, ensure_ascii=False, indent=2)}"""
+    else:
         # 已生成的因果鏈
         existing_text = ""
         if existing_chains:
@@ -203,7 +359,7 @@ def get_step2_dynamic_causal_chain_prompt(
                 " → ".join([chain.get(cat.name if hasattr(cat, 'name') else cat['name'], '?') for cat in sorted_cats])
                 for chain in existing_chains
             ]
-        existing_text = f"\n【已生成，請勿重複】\n" + "\n".join([f"- {c}" for c in chains_list])
+            existing_text = "\n【已生成，請勿重複】\n" + "\n".join([f"- {c}" for c in chains_list])
 
         # JSON 模板
         json_template = {cat.name if hasattr(cat, 'name') else cat['name']: f"選擇的{cat.name if hasattr(cat, 'name') else cat['name']}" for cat in sorted_cats}
@@ -230,19 +386,45 @@ def get_step2_dag_relationships_prompt(
     query: str,
     categories: List,  # List[CategoryDefinition]
     attributes_by_category: Dict[str, List[str]],
+    lang: LanguageType = "zh"
 ) -> str:
-    """生成相鄰類別之間的自然關係"""
+    """Generate natural relationships between adjacent categories"""
     sorted_cats = sorted(categories, key=lambda x: x.order if hasattr(x, 'order') else x.get('order', 0))
 
     # Build attribute listing
     attr_listing = "\n".join([
-        f"【{cat.name if hasattr(cat, 'name') else cat['name']}】{', '.join(attributes_by_category.get(cat.name if hasattr(cat, 'name') else cat['name'], []))}"
+        f"[{cat.name if hasattr(cat, 'name') else cat['name']}] {', '.join(attributes_by_category.get(cat.name if hasattr(cat, 'name') else cat['name'], []))}"
         for cat in sorted_cats
     ])
 
     # Build direction hints
     direction_hints = " → ".join([cat.name if hasattr(cat, 'name') else cat['name'] for cat in sorted_cats])
 
+    if lang == "en":
+        return f"""/no_think
+Analyze the attribute relationships of "{query}".
+
+{attr_listing}
+
+[Relationship Direction] {direction_hints}
+
+[Rules]
+1. Only establish relationships between adjacent categories (e.g., Materials→Functions, Functions→Usages)
+2. Only output pairs that have true causal or associative relationships
+3. An attribute can connect to multiple downstream attributes, or none at all
+4. Not every attribute needs to have connections
+5. Relationships should be reasonable and meaningful
+
+Return JSON:
+{{
+  "relationships": [
+    {{"source_category": "CategoryA", "source": "attribute name", "target_category": "CategoryB", "target": "attribute name"}},
+    ...
+  ]
+}}
+
+Return JSON only."""
+    else:
         return f"""/no_think
 分析「{query}」的屬性關係。
 

backend/app/prompts/expert_transformation_prompt.py

@@ -1,24 +1,58 @@
-"""Expert Transformation Agent 提示詞模組"""
+"""Expert Transformation Agent prompts module - Bilingual support"""
 
 from typing import List, Optional
+from .language_config import LanguageType
 
 
 def get_expert_generation_prompt(
     query: str,
     categories: List[str],
     expert_count: int,
-    custom_experts: Optional[List[str]] = None
+    custom_experts: Optional[List[str]] = None,
+    lang: LanguageType = "zh"
 ) -> str:
-    """Step 0: 生成專家團隊（不依賴主題，純隨機多元）"""
+    """Step 0: Generate expert team (not dependent on topic, purely random and diverse)"""
     import time
     import random
 
+    # Add timestamp and random number for diversity
+    seed = int(time.time() * 1000) % 10000
+
+    if lang == "en":
+        custom_text = ""
+        if custom_experts and len(custom_experts) > 0:
+            custom_text = f" (Specified: {', '.join(custom_experts[:expert_count])})"
+
+        diversity_hints = [
+            "obscure, non-mainstream, cross-disciplinary",
+            "rare occupations, emerging fields, fringe disciplines",
+            "unconventional, innovative, niche specialties",
+            "future-oriented, experimental, non-traditional",
+            "cross-cultural, hybrid fields, unique perspectives"
+        ]
+        hint = random.choice(diversity_hints)
+
+        return f"""/no_think
+Randomly assemble a team of {expert_count} experts from completely different fields{custom_text}.
+
+[Innovation Requirements] (Random seed: {seed})
+- Prioritize {hint} experts
+- Avoid common professions (such as doctors, engineers, teachers, lawyers, etc.)
+- Each expert must be from a completely unrelated field
+- The rarer and more innovative, the better
+
+Return JSON:
+{{"experts": [{{"id": "expert-0", "name": "profession", "domain": "field", "perspective": "viewpoint"}}, ...]}}
+
+Rules:
+- id should be expert-0 to expert-{expert_count - 1}
+- name is the profession name (not a person's name), 2-5 words
+- domain should be specific and unique, no duplicate types"""
+    else:
         custom_text = ""
         if custom_experts and len(custom_experts) > 0:
             custom_text = f"（已指定：{', '.join(custom_experts[:expert_count])}）"
 
-    # 加入時間戳和隨機數來增加多樣性
-    seed = int(time.time() * 1000) % 10000
         diversity_hints = [
             "冷門、非主流、跨領域",
             "罕見職業、新興領域、邊緣學科",
@@ -50,12 +84,38 @@ def get_expert_keyword_generation_prompt(
     category: str,
     attribute: str,
     experts: List[dict],  # List[ExpertProfile]
-    keywords_per_expert: int = 1
+    keywords_per_expert: int = 1,
+    lang: LanguageType = "zh"
 ) -> str:
-    """Step 1: 專家視角關鍵字生成"""
-    # 建立專家列表，格式更清晰
+    """Step 1: Expert perspective keyword generation"""
+    # Build expert list in clearer format
     experts_list = "\n".join([f"- {exp['id']}: {exp['name']}" for exp in experts])
 
+    if lang == "en":
+        return f"""/no_think
+You need to play the role of the following experts to generate innovative keywords for an attribute:
+
+[Expert List]
+{experts_list}
+
+[Task]
+Attribute: "{attribute}" (Category: {category})
+
+For each expert, please:
+1. First understand the professional background, knowledge domain, and work content of that profession
+2. Think about "{attribute}" from that profession's unique perspective
+3. Generate {keywords_per_expert} innovative keyword(s) related to that specialty (2-6 words)
+
+Keywords must reflect that expert's professional thinking style, for example:
+- Accountant viewing "movement" → "cash flow", "cost-benefit"
+- Architect viewing "movement" → "circulation design", "spatial flow"
+- Psychologist viewing "movement" → "behavioral motivation", "emotional transition"
+
+Return JSON:
+{{"keywords": [{{"keyword": "term", "expert_id": "expert-X", "expert_name": "name"}}, ...]}}
+
+Total of {len(experts) * keywords_per_expert} keywords needed, each keyword must be clearly related to the corresponding expert's professional field."""
+    else:
         return f"""/no_think
 你需要扮演以下專家，為屬性生成創新關鍵字：
 
@@ -86,9 +146,25 @@ def get_single_description_prompt(
     keyword: str,
     expert_id: str,
     expert_name: str,
-    expert_domain: str
+    expert_domain: str,
+    lang: LanguageType = "zh"
 ) -> str:
-    """Step 2: 為單一關鍵字生成描述"""
+    """Step 2: Generate description for a single keyword"""
+    if lang == "en":
+        # If domain is generic, just use profession name
+        domain_text = f" ({expert_domain} field)" if expert_domain and expert_domain != "Professional Field" else ""
+
+        return f"""/no_think
+You are a {expert_name}{domain_text}.
+
+Task: Generate an innovative application description for "{query}".
+Keyword: {keyword}
+
+From your professional perspective, explain how to apply the concept of "{keyword}" to "{query}". The description should be specific, creative, 15-30 words.
+
+Return JSON only, no other text:
+{{"description": "your innovative application description"}}"""
+    else:
         # 如果 domain 是通用的，就只用職業名稱
         domain_text = f"（{expert_domain}領域）" if expert_domain and expert_domain != "Professional Field" else ""
 

backend/app/prompts/language_config.py

@@ -0,0 +1,51 @@
+"""Language configuration for prompts"""
+
+from enum import Enum
+from typing import Literal
+
+class Language(str, Enum):
+    CHINESE = "zh"
+    ENGLISH = "en"
+
+LanguageType = Literal["zh", "en"]
+
+# Default categories for each language
+DEFAULT_CATEGORIES = {
+    "zh": ["材料", "功能", "用途", "使用族群", "特性"],
+    "en": ["Materials", "Functions", "Usages", "User Groups", "Characteristics"],
+}
+
+CATEGORY_DESCRIPTIONS = {
+    "zh": {
+        "材料": "物件由什麼材料組成",
+        "功能": "物件能做什麼",
+        "用途": "物件在什麼場景使用",
+        "使用族群": "誰會使用這個物件",
+        "特性": "物件有什麼特徵",
+    },
+    "en": {
+        "Materials": "What materials the object is made of",
+        "Functions": "What the object can do",
+        "Usages": "In what scenarios the object is used",
+        "User Groups": "Who uses this object",
+        "Characteristics": "What features the object has",
+    },
+}
+
+# Category name mappings between languages
+CATEGORY_MAPPING = {
+    "zh_to_en": {
+        "材料": "Materials",
+        "功能": "Functions",
+        "用途": "Usages",
+        "使用族群": "User Groups",
+        "特性": "Characteristics",
+    },
+    "en_to_zh": {
+        "Materials": "材料",
+        "Functions": "功能",
+        "Usages": "用途",
+        "User Groups": "使用族群",
+        "Characteristics": "特性",
+    },
+}

backend/app/prompts/transformation_prompt.py

@@ -1,21 +1,42 @@
-"""Transformation Agent 提示詞模組"""
+"""Transformation Agent prompts module - Bilingual support"""
 
 from typing import List
+from .language_config import LanguageType
 
 
 def get_keyword_generation_prompt(
     category: str,
     attributes: List[str],
-    keyword_count: int = 3
+    keyword_count: int = 3,
+    lang: LanguageType = "zh"
 ) -> str:
     """
-    Step 1: 生成新關鍵字
+    Step 1: Generate new keywords
 
-    給定類別和現有屬性，生成全新的、有創意的關鍵字。
-    不考慮原始查詢，只專注於類別本身可能的延伸。
+    Given a category and existing attributes, generate new, creative keywords.
+    Don't consider the original query, focus only on possible extensions of the category itself.
     """
-    attrs_text = "、".join(attributes)
+    attrs_text = ", ".join(attributes) if lang == "en" else "、".join(attributes)
 
+    if lang == "en":
+        return f"""/no_think
+You are a creative brainstorming expert. Given a category and its existing attributes, please generate new, creative keywords or descriptive phrases.
+
+[Category] {category}
+[Existing Attributes] {attrs_text}
+
+[Important Rules]
+1. Generate {keyword_count} completely new keywords
+2. Keywords must fit within the scope of "{category}" category
+3. Keywords should be creative and not duplicate or be too similar to existing attributes
+4. Don't consider any specific object, focus only on possible extensions of this category
+5. Each keyword should be 2-6 words
+
+Return JSON only:
+{{
+  "keywords": ["keyword1", "keyword2", "keyword3"]
+}}"""
+    else:
         return f"""/no_think
 你是一個創意發想專家。給定一個類別和該類別下的現有屬性，請生成全新的、有創意的關鍵字或描述片段。
 
@@ -38,13 +59,35 @@ def get_keyword_generation_prompt(
 def get_description_generation_prompt(
     query: str,
     category: str,
-    keyword: str
+    keyword: str,
+    lang: LanguageType = "zh"
 ) -> str:
     """
-    Step 2: 結合原始查詢生成描述
+    Step 2: Combine with original query to generate description
 
-    用新關鍵字創造一個與原始查詢相關的創新應用描述。
+    Use new keyword to create an innovative application description related to the original query.
     """
+    if lang == "en":
+        return f"""/no_think
+You are an innovation application expert. Please apply a new keyword concept to a specific object to create an innovative application description.
+
+[Object] {query}
+[Category] {category}
+[New Keyword] {keyword}
+
+[Task]
+Using the concept of "{keyword}", create an innovative application description for "{query}".
+The description should be a complete sentence or phrase explaining how to apply this new concept to the object.
+
+[Example Format]
+- If the object is "bicycle" and keyword is "monitor", you could generate "bicycle monitors the rider's health status"
+- If the object is "umbrella" and keyword is "generate power", you could generate "umbrella generates electricity using raindrop impacts"
+
+Return JSON only:
+{{
+  "description": "innovative application description"
+}}"""
+    else:
         return f"""/no_think
 你是一個創新應用專家。請將一個新的關鍵字概念應用到特定物件上，創造出創新的應用描述。
 
@@ -69,14 +112,34 @@ def get_description_generation_prompt(
 def get_batch_description_prompt(
     query: str,
     category: str,
-    keywords: List[str]
+    keywords: List[str],
+    lang: LanguageType = "zh"
 ) -> str:
     """
-    批次生成描述（可選的優化版本，一次處理多個關鍵字）
+    Batch description generation (optional optimized version, process multiple keywords at once)
     """
-    keywords_text = "、".join(keywords)
-    keywords_json = ", ".join([f'"{k}"' for k in keywords])
+    keywords_text = ", ".join(keywords) if lang == "en" else "、".join(keywords)
 
+    if lang == "en":
+        return f"""/no_think
+You are an innovation application expert. Please apply multiple new keyword concepts to a specific object, creating an innovative application description for each keyword.
+
+[Object] {query}
+[Category] {category}
+[New Keywords] {keywords_text}
+
+[Task]
+Create an innovative application description related to "{query}" for each keyword.
+Each description should be a complete sentence or phrase.
+
+Return JSON only:
+{{
+  "descriptions": [
+    {{"keyword": "keyword1", "description": "description1"}},
+    {{"keyword": "keyword2", "description": "description2"}}
+  ]
+}}"""
+    else:
         return f"""/no_think
 你是一個創新應用專家。請將多個新的關鍵字概念應用到特定物件上，為每個關鍵字創造創新的應用描述。
 

backend/app/routers/attributes.py

@@ -58,7 +58,8 @@ async def execute_step0(
     prompt = get_step0_category_analysis_prompt(
         request.query,
         request.suggested_category_count,
-        exclude_categories=exclude_categories
+        exclude_categories=exclude_categories,
+        lang=request.lang
     )
     temperature = request.temperature if request.temperature is not None else 0.7
     response = await ollama_provider.generate(
@@ -310,7 +311,7 @@ async def generate_sse_events(request: StreamAnalyzeRequest) -> AsyncGenerator[s
         # ========== Step 1: Generate Attributes (Dynamic) ==========
         yield f"event: step1_start\ndata: {json.dumps({'message': '生成屬性...'}, ensure_ascii=False)}\n\n"
 
-        step1_prompt = get_step1_dynamic_attributes_prompt(request.query, final_categories)
+        step1_prompt = get_step1_dynamic_attributes_prompt(request.query, final_categories, lang=request.lang)
         logger.info(f"Step 1 prompt: {step1_prompt[:200]}")
 
         step1_response = await ollama_provider.generate(
@@ -330,6 +331,7 @@ async def generate_sse_events(request: StreamAnalyzeRequest) -> AsyncGenerator[s
             query=request.query,
             categories=final_categories,
             attributes_by_category=step1_result.attributes,
+            lang=request.lang
         )
         logger.info(f"Step 2 (relationships) prompt: {step2_prompt[:300]}")
 

backend/app/routers/deduplication.py

@@ -63,7 +63,8 @@ async def deduplicate_descriptions(request: DeduplicationRequest) -> Deduplicati
             # 使用 LLM 成對比較去重
             result = await llm_deduplication_service.deduplicate(
                 descriptions=request.descriptions,
-                model=request.model
+                model=request.model,
+                lang=request.lang
             )
         return result
     except ValueError as e:

backend/app/routers/expert_transformation.py

@@ -68,7 +68,8 @@ async def generate_expert_transformation_events(
                     query=request.query,
                     categories=all_categories,
                     expert_count=request.expert_count,
-                    custom_experts=actual_custom_experts if actual_custom_experts else None
+                    custom_experts=actual_custom_experts if actual_custom_experts else None,
+                    lang=request.lang
                 )
                 logger.info(f"Expert prompt: {expert_prompt[:200]}")
 
@@ -119,7 +120,8 @@ async def generate_expert_transformation_events(
                         query=request.query,
                         categories=all_categories,
                         expert_count=request.expert_count,
-                        custom_experts=actual_custom_experts if actual_custom_experts else None
+                        custom_experts=actual_custom_experts if actual_custom_experts else None,
+                        lang=request.lang
                     )
 
                     expert_response = await ollama_provider.generate(
@@ -160,7 +162,8 @@ async def generate_expert_transformation_events(
                     category=request.category,
                     attribute=attribute,
                     experts=[e.model_dump() for e in experts],
-                    keywords_per_expert=request.keywords_per_expert
+                    keywords_per_expert=request.keywords_per_expert,
+                    lang=request.lang
                 )
                 logger.info(f"Keyword prompt for '{attribute}': {kw_prompt[:300]}")
 
@@ -214,7 +217,8 @@ async def generate_expert_transformation_events(
                     keyword=kw.keyword,
                     expert_id=kw.expert_id,
                     expert_name=kw.expert_name,
-                    expert_domain=expert_domain
+                    expert_domain=expert_domain,
+                    lang=request.lang
                 )
 
                 desc_response = await ollama_provider.generate(

backend/app/routers/patent_search.py

@@ -0,0 +1,133 @@
+"""Patent Search Router - Search for similar patents"""
+
+import logging
+from typing import Optional, List
+
+from fastapi import APIRouter
+from pydantic import BaseModel
+
+from ..services.patent_search_service import patent_search_service
+
+logger = logging.getLogger(__name__)
+router = APIRouter(prefix="/api/patent", tags=["patent"])
+
+
+# ===== Request/Response Models =====
+
+class PatentSearchRequest(BaseModel):
+    """Patent search request"""
+    query: str                          # Search query (description or keywords)
+    max_results: int = 10               # Maximum results to return (1-20)
+
+
+class PatentResult(BaseModel):
+    """Single patent result"""
+    publication_number: str
+    title: str
+    snippet: str
+    publication_date: Optional[str] = None
+    assignee: Optional[str] = None
+    inventor: Optional[str] = None
+    status: str                         # ACTIVE, NOT_ACTIVE, UNKNOWN
+    pdf_url: Optional[str] = None
+    thumbnail_url: Optional[str] = None
+
+
+class PatentSearchResponse(BaseModel):
+    """Patent search response"""
+    query: str
+    total_results: int
+    patents: List[PatentResult]
+    error: Optional[str] = None
+
+
+class BatchPatentSearchRequest(BaseModel):
+    """Batch patent search request - search multiple descriptions"""
+    queries: List[str]                  # List of descriptions to search
+    max_results_per_query: int = 5      # Max results per query
+
+
+class BatchPatentSearchResult(BaseModel):
+    """Results for a single query in batch search"""
+    query: str
+    total_results: int
+    patents: List[PatentResult]
+    error: Optional[str] = None
+
+
+class BatchPatentSearchResponse(BaseModel):
+    """Batch patent search response"""
+    results: List[BatchPatentSearchResult]
+    total_queries: int
+
+
+# ===== Endpoints =====
+
+@router.post("/search", response_model=PatentSearchResponse)
+async def search_patents(request: PatentSearchRequest):
+    """
+    Search for patents similar to the given description/query.
+
+    Uses Google Patents to find related patents based on keywords.
+    """
+    logger.info(f"Patent search request: {request.query[:100]}...")
+
+    # Limit max_results to reasonable range
+    max_results = min(max(1, request.max_results), 20)
+
+    result = await patent_search_service.search(
+        query=request.query,
+        max_results=max_results,
+    )
+
+    return PatentSearchResponse(
+        query=request.query,
+        total_results=result.get("total_results", 0),
+        patents=[PatentResult(**p) for p in result.get("patents", [])],
+        error=result.get("error"),
+    )
+
+
+@router.post("/search/batch", response_model=BatchPatentSearchResponse)
+async def batch_search_patents(request: BatchPatentSearchRequest):
+    """
+    Search for patents for multiple descriptions at once.
+
+    Useful for checking multiple creative descriptions against patents.
+    """
+    logger.info(f"Batch patent search: {len(request.queries)} queries")
+
+    # Limit results per query
+    max_per_query = min(max(1, request.max_results_per_query), 10)
+
+    results: List[BatchPatentSearchResult] = []
+
+    for query in request.queries:
+        result = await patent_search_service.search(
+            query=query,
+            max_results=max_per_query,
+        )
+
+        results.append(BatchPatentSearchResult(
+            query=query,
+            total_results=result.get("total_results", 0),
+            patents=[PatentResult(**p) for p in result.get("patents", [])],
+            error=result.get("error"),
+        ))
+
+    return BatchPatentSearchResponse(
+        results=results,
+        total_queries=len(request.queries),
+    )
+
+
+@router.get("/health")
+async def patent_search_health():
+    """Check if patent search service is working"""
+    # Do a simple test search
+    result = await patent_search_service.search("test", max_results=1)
+
+    if result.get("error"):
+        return {"status": "unhealthy", "error": result["error"]}
+
+    return {"status": "healthy"}

backend/app/routers/transformation.py

@@ -36,7 +36,8 @@ async def generate_transformation_events(
         keyword_prompt = get_keyword_generation_prompt(
             category=request.category,
             attributes=request.attributes,
-            keyword_count=request.keyword_count
+            keyword_count=request.keyword_count,
+            lang=request.lang
         )
         logger.info(f"Keyword prompt: {keyword_prompt[:200]}")
 
@@ -61,7 +62,8 @@ async def generate_transformation_events(
         desc_prompt = get_batch_description_prompt(
             query=request.query,
             category=request.category,
-            keywords=new_keywords
+            keywords=new_keywords,
+            lang=request.lang
         )
         logger.info(f"Description prompt: {desc_prompt[:300]}")
 

backend/app/services/llm_deduplication_service.py

@@ -1,12 +1,12 @@
 """
-LLM Deduplication Service - 使用 LLM 成對比較進行去重
+LLM Deduplication Service - Using LLM pairwise comparison for deduplication
 
-讓 LLM 判斷兩個描述是否語意重複，透過並行處理加速。
+Let LLM determine whether two descriptions are semantically duplicate, accelerated by parallel processing.
 """
 
 import asyncio
 import logging
-from typing import List, Tuple, Optional
+from typing import List, Tuple, Optional, Literal
 
 import httpx
 import numpy as np
@@ -18,6 +18,7 @@ from ..models.schemas import (
     DeduplicationMethod,
     DescriptionGroup,
 )
+from ..prompts.language_config import LanguageType
 
 logger = logging.getLogger(__name__)
 
@@ -31,27 +32,20 @@ class LLMDeduplicationService:
         self.client = httpx.AsyncClient(timeout=60.0)
         self.max_concurrent = 5  # 最大並行數，避免 Ollama 過載
 
-    async def compare_pair(
-        self,
-        desc1: str,
-        desc2: str,
-        model: str,
-        semaphore: asyncio.Semaphore
-    ) -> bool:
-        """
-        讓 LLM 判斷兩個描述是否語意重複
+    def _get_comparison_prompt(self, desc1: str, desc2: str, lang: LanguageType = "zh") -> str:
+        """Get comparison prompt in the specified language"""
+        if lang == "en":
+            return f"""Determine whether the following two innovative descriptions express the same or very similar concepts:
 
-        Args:
-            desc1: 第一個描述
-            desc2: 第二個描述
-            model: LLM 模型名稱
-            semaphore: 並行控制信號量
+Description 1: {desc1}
 
-        Returns:
-            bool: 是否為重複描述
-        """
-        async with semaphore:  # 控制並行數
-            prompt = f"""判斷以下兩個創新描述是否表達相同或非常相似的概念：
+Description 2: {desc2}
+
+If both descriptions essentially express the same or very similar innovative concept, answer "YES"
+If the two descriptions express different innovative concepts, answer "NO"
+Only answer YES or NO, no other text"""
+        else:
+            return f"""判斷以下兩個創新描述是否表達相同或非常相似的概念：
 
 描述1: {desc1}
 
@@ -61,6 +55,30 @@ class LLMDeduplicationService:
 如果兩者描述不同的創新概念，回答 "NO"
 只回答 YES 或 NO，不要其他文字"""
 
+    async def compare_pair(
+        self,
+        desc1: str,
+        desc2: str,
+        model: str,
+        semaphore: asyncio.Semaphore,
+        lang: LanguageType = "zh"
+    ) -> bool:
+        """
+        Let LLM determine whether two descriptions are semantically duplicate
+
+        Args:
+            desc1: First description
+            desc2: Second description
+            model: LLM model name
+            semaphore: Concurrency control semaphore
+            lang: Language for the prompt
+
+        Returns:
+            bool: Whether the descriptions are duplicates
+        """
+        async with semaphore:  # Control concurrency
+            prompt = self._get_comparison_prompt(desc1, desc2, lang)
+
             try:
                 response = await self.client.post(
                     f"{self.base_url}/api/generate",
@@ -86,26 +104,28 @@ class LLMDeduplicationService:
     async def compare_batch(
         self,
         pairs: List[Tuple[int, int, str, str]],
-        model: str
+        model: str,
+        lang: LanguageType = "zh"
     ) -> List[Tuple[int, int, bool]]:
         """
-        並行批次比較多個描述對
+        Parallel batch comparison of multiple description pairs
 
         Args:
-            pairs: 待比較的配對列表 [(i, j, desc1, desc2), ...]
-            model: LLM 模型名稱
+            pairs: List of pairs to compare [(i, j, desc1, desc2), ...]
+            model: LLM model name
+            lang: Language for the prompt
 
         Returns:
-            比較結果列表 [(i, j, is_similar), ...]
+            List of comparison results [(i, j, is_similar), ...]
         """
         semaphore = asyncio.Semaphore(self.max_concurrent)
 
         async def compare_one(pair: Tuple[int, int, str, str]) -> Tuple[int, int, bool]:
             i, j, desc1, desc2 = pair
-            is_similar = await self.compare_pair(desc1, desc2, model, semaphore)
+            is_similar = await self.compare_pair(desc1, desc2, model, semaphore, lang)
             return (i, j, is_similar)
 
-        # 使用 asyncio.gather 並行執行所有比較
+        # Use asyncio.gather to execute all comparisons in parallel
         results = await asyncio.gather(*[compare_one(p) for p in pairs])
         return results
 
@@ -144,17 +164,19 @@ class LLMDeduplicationService:
     async def deduplicate(
         self,
         descriptions: List[ExpertTransformationDescription],
-        model: Optional[str] = None
+        model: Optional[str] = None,
+        lang: LanguageType = "zh"
     ) -> DeduplicationResult:
         """
-        使用 LLM 成對比較進行去重
+        Use LLM pairwise comparison for deduplication
 
         Args:
-            descriptions: 要去重的描述列表
-            model: LLM 模型名稱
+            descriptions: List of descriptions to deduplicate
+            model: LLM model name
+            lang: Language for the prompt
 
         Returns:
-            DeduplicationResult: 去重結果
+            DeduplicationResult: Deduplication result
         """
         model = model or self.default_model
 
@@ -188,10 +210,10 @@ class LLMDeduplicationService:
                 ))
 
         total_pairs = len(pairs)
-        logger.info(f"LLM deduplication: {total_pairs} pairs to compare (parallel={self.max_concurrent}, model={model})")
+        logger.info(f"LLM deduplication: {total_pairs} pairs to compare (parallel={self.max_concurrent}, model={model}, lang={lang})")
 
-        # 並行批次比較
-        results = await self.compare_batch(pairs, model)
+        # Parallel batch comparison
+        results = await self.compare_batch(pairs, model, lang)
 
         # 填入相似度矩陣
         for i, j, is_similar in results:

backend/app/services/patent_search_service.py

@@ -0,0 +1,195 @@
+"""Patent Search Service using Google Patents XHR API"""
+
+import httpx
+import logging
+from typing import List, Optional
+from urllib.parse import quote_plus
+
+logger = logging.getLogger(__name__)
+
+
+class PatentSearchResult:
+    """Single patent search result"""
+    def __init__(
+        self,
+        publication_number: str,
+        title: str,
+        snippet: str,
+        publication_date: Optional[str],
+        assignee: Optional[str],
+        inventor: Optional[str],
+        status: str,
+        pdf_url: Optional[str] = None,
+        thumbnail_url: Optional[str] = None,
+    ):
+        self.publication_number = publication_number
+        self.title = title
+        self.snippet = snippet
+        self.publication_date = publication_date
+        self.assignee = assignee
+        self.inventor = inventor
+        self.status = status
+        self.pdf_url = pdf_url
+        self.thumbnail_url = thumbnail_url
+
+    def to_dict(self):
+        return {
+            "publication_number": self.publication_number,
+            "title": self.title,
+            "snippet": self.snippet,
+            "publication_date": self.publication_date,
+            "assignee": self.assignee,
+            "inventor": self.inventor,
+            "status": self.status,
+            "pdf_url": self.pdf_url,
+            "thumbnail_url": self.thumbnail_url,
+        }
+
+
+class PatentSearchService:
+    """Service for searching patents using Google Patents"""
+
+    GOOGLE_PATENTS_XHR_URL = "https://patents.google.com/xhr/query"
+    GOOGLE_PATENTS_PDF_BASE = "https://patentimages.storage.googleapis.com/"
+
+    def __init__(self):
+        self._client: Optional[httpx.AsyncClient] = None
+
+    # Browser-like headers to avoid being blocked
+    DEFAULT_HEADERS = {
+        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
+        "Accept": "application/json, text/plain, */*",
+        "Accept-Language": "en-US,en;q=0.9",
+        "Referer": "https://patents.google.com/",
+        "Origin": "https://patents.google.com",
+    }
+
+    async def _get_client(self) -> httpx.AsyncClient:
+        if self._client is None or self._client.is_closed:
+            self._client = httpx.AsyncClient(
+                timeout=30.0,
+                headers=self.DEFAULT_HEADERS,
+                follow_redirects=True,
+            )
+        return self._client
+
+    async def close(self):
+        if self._client and not self._client.is_closed:
+            await self._client.aclose()
+
+    async def search(
+        self,
+        query: str,
+        max_results: int = 10,
+    ) -> dict:
+        """
+        Search Google Patents for relevant patents
+
+        Args:
+            query: Search query (can be a description or keywords)
+            max_results: Maximum number of results to return
+
+        Returns:
+            Dict with total_results count and list of patent results
+        """
+        try:
+            client = await self._get_client()
+
+            # URL encode the query
+            encoded_query = quote_plus(query)
+            url = f"{self.GOOGLE_PATENTS_XHR_URL}?url=q%3D{encoded_query}&exp=&tags="
+
+            logger.info(f"Searching patents with query: {query[:100]}...")
+
+            response = await client.get(url)
+
+            if response.status_code != 200:
+                logger.error(f"Google Patents API returned status {response.status_code}")
+                return {
+                    "total_results": 0,
+                    "patents": [],
+                    "error": f"API returned status {response.status_code}"
+                }
+
+            data = response.json()
+
+            # Parse results
+            results = data.get("results", {})
+            total_num = results.get("total_num_results", 0)
+            clusters = results.get("cluster", [])
+
+            patents: List[PatentSearchResult] = []
+
+            if clusters and len(clusters) > 0:
+                patent_results = clusters[0].get("result", [])
+
+                for item in patent_results[:max_results]:
+                    patent_data = item.get("patent", {})
+                    family_meta = patent_data.get("family_metadata", {})
+                    aggregated = family_meta.get("aggregated", {})
+                    country_status = aggregated.get("country_status", [])
+
+                    status = "UNKNOWN"
+                    if country_status and len(country_status) > 0:
+                        best_stage = country_status[0].get("best_patent_stage", {})
+                        status = best_stage.get("state", "UNKNOWN")
+
+                    # Build PDF URL if available
+                    pdf_path = patent_data.get("pdf", "")
+                    pdf_url = f"{self.GOOGLE_PATENTS_PDF_BASE}{pdf_path}" if pdf_path else None
+
+                    # Build thumbnail URL
+                    thumbnail = patent_data.get("thumbnail", "")
+                    thumbnail_url = f"{self.GOOGLE_PATENTS_PDF_BASE}{thumbnail}" if thumbnail else None
+
+                    patent = PatentSearchResult(
+                        publication_number=patent_data.get("publication_number", ""),
+                        title=self._clean_html(patent_data.get("title", "")),
+                        snippet=self._clean_html(patent_data.get("snippet", "")),
+                        publication_date=patent_data.get("publication_date"),
+                        assignee=patent_data.get("assignee"),
+                        inventor=patent_data.get("inventor"),
+                        status=status,
+                        pdf_url=pdf_url,
+                        thumbnail_url=thumbnail_url,
+                    )
+                    patents.append(patent)
+
+            logger.info(f"Found {total_num} total patents, returning {len(patents)}")
+
+            return {
+                "total_results": total_num,
+                "patents": [p.to_dict() for p in patents],
+            }
+
+        except httpx.HTTPError as e:
+            logger.error(f"HTTP error searching patents: {e}")
+            return {
+                "total_results": 0,
+                "patents": [],
+                "error": str(e)
+            }
+        except Exception as e:
+            logger.error(f"Error searching patents: {e}")
+            return {
+                "total_results": 0,
+                "patents": [],
+                "error": str(e)
+            }
+
+    def _clean_html(self, text: str) -> str:
+        """Remove HTML entities and tags from text"""
+        if not text:
+            return ""
+        # Replace common HTML entities
+        text = text.replace("…", "...")
+        text = text.replace("&", "&")
+        text = text.replace("&lt;", "<")
+        text = text.replace("&gt;", ">")
+        text = text.replace("&quot;", '"')
+        text = text.replace("&#39;", "'")
+        return text.strip()
+
+
+# Singleton instance
+patent_search_service = PatentSearchService()

frontend/src/App.tsx

@@ -1,17 +1,24 @@
-import { useState, useRef, useCallback, useEffect } from 'react';
-import { ConfigProvider, Layout, theme, Typography, Space, Tabs, Slider, Radio } from 'antd';
-import { ApartmentOutlined, ThunderboltOutlined, FilterOutlined } from '@ant-design/icons';
+import { useState, useRef, useCallback, useEffect, useMemo } from 'react';
+import { ConfigProvider, Layout, theme, Typography, Space, Tabs, Slider, Radio, Switch, Segmented } from 'antd';
+import { ApartmentOutlined, ThunderboltOutlined, FilterOutlined, SwapOutlined, FileSearchOutlined, GlobalOutlined } from '@ant-design/icons';
 import { ThemeToggle } from './components/ThemeToggle';
 import { InputPanel } from './components/InputPanel';
 import { TransformationInputPanel } from './components/TransformationInputPanel';
 import { MindmapPanel } from './components/MindmapPanel';
 import { TransformationPanel } from './components/TransformationPanel';
 import { DeduplicationPanel } from './components/DeduplicationPanel';
+import { PatentSearchPanel } from './components/PatentSearchPanel';
+import { DualPathInputPanel } from './components/DualPathInputPanel';
+import { DualPathMindmapPanel } from './components/DualPathMindmapPanel';
+import { CrossoverPanel } from './components/CrossoverPanel';
 import { useAttribute } from './hooks/useAttribute';
+import { useDualPathAttribute } from './hooks/useDualPathAttribute';
 import { getModels } from './services/api';
+import { crossoverPairsToDAGs, type CrossoverDAGResult } from './utils/crossoverToDAG';
+import { DualTransformationPanel } from './components/DualTransformationPanel';
 import type { MindmapDAGRef } from './components/MindmapDAG';
 import type { TransformationDAGRef } from './components/TransformationDAG';
-import type { CategoryMode, ExpertSource, ExpertTransformationDAGResult, DeduplicationMethod } from './types';
+import type { CategoryMode, ExpertSource, ExpertTransformationDAGResult, DeduplicationMethod, ExpertMode, CrossoverPair, PromptLanguage } from './types';
 
 const { Header, Sider, Content } = Layout;
 const { Title } = Typography;
@@ -24,7 +31,15 @@ interface VisualSettings {
 function App() {
   const [isDark, setIsDark] = useState(true);
   const [activeTab, setActiveTab] = useState<string>('attribute');
+  const [dualPathMode, setDualPathMode] = useState(false);
+  const [promptLanguage, setPromptLanguage] = useState<PromptLanguage>('zh');
+
+  // Single path hook
   const { loading, progress, error, currentResult, history, analyze, loadFromHistory } = useAttribute();
+
+  // Dual path hook
+  const dualPath = useDualPathAttribute();
+
   const [visualSettings, setVisualSettings] = useState<VisualSettings>({
     nodeSpacing: 32,
     fontSize: 14,
@@ -32,6 +47,21 @@ function App() {
   const mindmapRef = useRef<MindmapDAGRef>(null);
   const transformationRef = useRef<TransformationDAGRef>(null);
 
+  // Dual path expert mode
+  const [expertMode, setExpertMode] = useState<ExpertMode>('shared');
+  const [selectedCrossoverPairs, setSelectedCrossoverPairs] = useState<CrossoverPair[]>([]);
+
+  // Convert selected crossover pairs to two separate DAGs for dual transformation
+  const crossoverDAGs = useMemo((): CrossoverDAGResult | null => {
+    if (selectedCrossoverPairs.length === 0) return null;
+    if (!dualPath.pathA.result || !dualPath.pathB.result) return null;
+    return crossoverPairsToDAGs(
+      selectedCrossoverPairs,
+      dualPath.pathA.result,
+      dualPath.pathB.result
+    );
+  }, [selectedCrossoverPairs, dualPath.pathA.result, dualPath.pathB.result]);
+
   // Transformation Agent settings
   const [transformModel, setTransformModel] = useState<string>('');
   const [transformTemperature, setTransformTemperature] = useState<number>(0.95);
@@ -83,9 +113,10 @@ function App() {
     chainCount?: number,
     categoryMode?: CategoryMode,
     customCategories?: string[],
-    suggestedCategoryCount?: number
+    suggestedCategoryCount?: number,
+    lang?: PromptLanguage
   ) => {
-    await analyze(query, model, temperature, chainCount, categoryMode, customCategories, suggestedCategoryCount);
+    await analyze(query, model, temperature, chainCount, categoryMode, customCategories, suggestedCategoryCount, lang || promptLanguage);
   };
 
   const handleResetView = useCallback(() => {
@@ -96,6 +127,30 @@ function App() {
     setShouldStartTransform(true);
   }, []);
 
+  // Dual path analysis handler
+  const handleDualPathAnalyze = useCallback(async (
+    queryA: string,
+    queryB: string,
+    options?: {
+      model?: string;
+      temperature?: number;
+      chainCount?: number;
+      categoryMode?: CategoryMode;
+      customCategories?: string[];
+      suggestedCategoryCount?: number;
+      lang?: PromptLanguage;
+    }
+  ) => {
+    await dualPath.analyzeParallel(queryA, queryB, { ...options, lang: options?.lang || promptLanguage });
+  }, [dualPath, promptLanguage]);
+
+  // Handle mode switch
+  const handleModeSwitch = useCallback((checked: boolean) => {
+    setDualPathMode(checked);
+    // Reset to attribute tab when switching modes
+    setActiveTab('attribute');
+  }, []);
+
   return (
     <ConfigProvider
       theme={{
@@ -140,7 +195,31 @@ function App() {
               Novelty Seeking
             </Title>
           </Space>
+          <Space align="center" size="middle">
+            <Space size="small">
+              <Typography.Text type="secondary">Single</Typography.Text>
+              <Switch
+                checked={dualPathMode}
+                onChange={handleModeSwitch}
+                checkedChildren={<SwapOutlined />}
+                unCheckedChildren={<ApartmentOutlined />}
+              />
+              <Typography.Text type="secondary">Dual</Typography.Text>
+            </Space>
+            <Space size="small">
+              <GlobalOutlined style={{ color: isDark ? '#177ddc' : '#1890ff' }} />
+              <Segmented
+                size="small"
+                value={promptLanguage}
+                onChange={(value) => setPromptLanguage(value as PromptLanguage)}
+                options={[
+                  { label: '中文', value: 'zh' },
+                  { label: 'EN', value: 'en' },
+                ]}
+              />
+            </Space>
             <ThemeToggle isDark={isDark} onToggle={setIsDark} />
+          </Space>
         </Header>
         <Layout>
           <Content
@@ -155,7 +234,98 @@ function App() {
               onChange={setActiveTab}
               style={{ height: '100%' }}
               tabBarStyle={{ marginBottom: 8 }}
-              items={[
+              items={dualPathMode ? [
+                // ===== Dual Path Mode Tabs =====
+                {
+                  key: 'attribute',
+                  label: (
+                    <span>
+                      <SwapOutlined style={{ marginRight: 8 }} />
+                      Dual Path Attribute
+                    </span>
+                  ),
+                  children: (
+                    <div style={{ height: 'calc(100vh - 140px)' }}>
+                      <DualPathMindmapPanel
+                        pathA={dualPath.pathA}
+                        pathB={dualPath.pathB}
+                        isDark={isDark}
+                        visualSettings={visualSettings}
+                      />
+                    </div>
+                  ),
+                },
+                {
+                  key: 'crossover',
+                  label: (
+                    <span>
+                      <SwapOutlined style={{ marginRight: 8 }} />
+                      Crossover
+                    </span>
+                  ),
+                  children: (
+                    <div style={{ height: 'calc(100vh - 140px)', padding: 16 }}>
+                      <CrossoverPanel
+                        pathAResult={dualPath.pathA.result}
+                        pathBResult={dualPath.pathB.result}
+                        isDark={isDark}
+                        expertMode={expertMode}
+                        onExpertModeChange={setExpertMode}
+                        onCrossoverReady={setSelectedCrossoverPairs}
+                      />
+                    </div>
+                  ),
+                },
+                {
+                  key: 'transformation',
+                  label: (
+                    <span>
+                      <ThunderboltOutlined style={{ marginRight: 8 }} />
+                      Transformation Agent
+                      {crossoverDAGs && (
+                        <span style={{ marginLeft: 4, fontSize: 10, opacity: 0.7 }}>
+                          (A:{crossoverDAGs.pathA.nodes.length} / B:{crossoverDAGs.pathB.nodes.length})
+                        </span>
+                      )}
+                    </span>
+                  ),
+                  children: (
+                    <div style={{ height: 'calc(100vh - 140px)' }}>
+                      <DualTransformationPanel
+                        crossoverDAGA={crossoverDAGs?.pathA ?? null}
+                        crossoverDAGB={crossoverDAGs?.pathB ?? null}
+                        isDark={isDark}
+                        model={transformModel}
+                        temperature={transformTemperature}
+                        expertConfig={expertConfig}
+                        expertSource={expertSource}
+                        expertLanguage={expertLanguage}
+                        lang={promptLanguage}
+                        shouldStartTransform={shouldStartTransform}
+                        onTransformComplete={() => setShouldStartTransform(false)}
+                        onLoadingChange={setTransformLoading}
+                      />
+                    </div>
+                  ),
+                },
+                {
+                  key: 'patent',
+                  label: (
+                    <span>
+                      <FileSearchOutlined style={{ marginRight: 8 }} />
+                      Patent Search
+                    </span>
+                  ),
+                  children: (
+                    <div style={{ height: 'calc(100vh - 140px)' }}>
+                      <PatentSearchPanel
+                        isDark={isDark}
+                      />
+                    </div>
+                  ),
+                },
+              ] : [
+                // ===== Single Path Mode Tabs =====
                 {
                   key: 'attribute',
                   label: (
@@ -196,6 +366,7 @@ function App() {
                         expertConfig={expertConfig}
                         expertSource={expertSource}
                         expertLanguage={expertLanguage}
+                        lang={promptLanguage}
                         shouldStartTransform={shouldStartTransform}
                         onTransformComplete={() => setShouldStartTransform(false)}
                         onLoadingChange={setTransformLoading}
@@ -221,6 +392,24 @@ function App() {
                         onThresholdChange={setDeduplicationThreshold}
                         method={deduplicationMethod}
                         onMethodChange={setDeduplicationMethod}
+                        lang={promptLanguage}
+                      />
+                    </div>
+                  ),
+                },
+                {
+                  key: 'patent',
+                  label: (
+                    <span>
+                      <FileSearchOutlined style={{ marginRight: 8 }} />
+                      Patent Search
+                    </span>
+                  ),
+                  children: (
+                    <div style={{ height: 'calc(100vh - 140px)' }}>
+                      <PatentSearchPanel
+                        descriptions={transformationResult?.results.flatMap(r => r.descriptions)}
+                        isDark={isDark}
                       />
                     </div>
                   ),
@@ -236,24 +425,54 @@ function App() {
               overflow: 'auto',
             }}
           >
-            {activeTab === 'attribute' && (
+            {activeTab === 'attribute' && !dualPathMode && (
               <InputPanel
                 loading={loading}
                 progress={progress}
                 history={history}
                 currentResult={currentResult}
                 onAnalyze={handleAnalyze}
-                onLoadHistory={loadFromHistory}
+                onLoadHistory={(item, lang) => loadFromHistory(item, lang || promptLanguage)}
                 onResetView={handleResetView}
                 visualSettings={visualSettings}
                 onVisualSettingsChange={setVisualSettings}
+                lang={promptLanguage}
               />
             )}
+            {activeTab === 'attribute' && dualPathMode && (
+              <DualPathInputPanel
+                onAnalyze={handleDualPathAnalyze}
+                loadingA={dualPath.pathA.loading}
+                loadingB={dualPath.pathB.loading}
+                progressA={dualPath.pathA.progress}
+                progressB={dualPath.pathB.progress}
+                availableModels={availableModels}
+                lang={promptLanguage}
+              />
+            )}
+            {activeTab === 'crossover' && dualPathMode && (
+              <div style={{ padding: 16 }}>
+                <Typography.Title level={5} style={{ marginBottom: 16 }}>
+                  <SwapOutlined style={{ marginRight: 8 }} />
+                  Crossover Settings
+                </Typography.Title>
+                <Typography.Text type="secondary">
+                  Select attribute pairs in the main panel to create crossover combinations.
+                  {selectedCrossoverPairs.length > 0 && (
+                    <div style={{ marginTop: 8 }}>
+                      <Typography.Text strong>
+                        {selectedCrossoverPairs.length} pairs selected
+                      </Typography.Text>
+                    </div>
+                  )}
+                </Typography.Text>
+              </div>
+            )}
             {activeTab === 'transformation' && (
               <TransformationInputPanel
                 onTransform={handleTransform}
                 loading={transformLoading}
-                hasData={!!currentResult}
+                hasData={dualPathMode ? !!crossoverDAGs : !!currentResult}
                 isDark={isDark}
                 model={transformModel}
                 temperature={transformTemperature}

frontend/src/components/CrossoverPanel.tsx

@@ -0,0 +1,298 @@
+import { useEffect, useState } from 'react';
+import {
+  Empty,
+  Card,
+  Button,
+  Statistic,
+  Row,
+  Col,
+  Typography,
+  Space,
+  Badge,
+  Collapse,
+  Checkbox,
+  Radio,
+} from 'antd';
+import {
+  SwapOutlined,
+  CheckCircleOutlined,
+  ReloadOutlined,
+  UnorderedListOutlined,
+  TableOutlined,
+} from '@ant-design/icons';
+import type { AttributeDAG, CrossoverPair, ExpertMode } from '../types';
+import { useAttributeCrossover } from '../hooks/useAttributeCrossover';
+import { CrossoverCard } from './crossover/CrossoverCard';
+import { CrossoverMatrix } from './crossover/CrossoverMatrix';
+import { CrossoverPreview } from './crossover/CrossoverPreview';
+
+const { Text } = Typography;
+
+interface CrossoverPanelProps {
+  pathAResult: AttributeDAG | null;
+  pathBResult: AttributeDAG | null;
+  isDark: boolean;
+  expertMode: ExpertMode;
+  onExpertModeChange: (mode: ExpertMode) => void;
+  onCrossoverReady?: (selectedPairs: CrossoverPair[]) => void;
+}
+
+type ViewMode = 'list' | 'matrix';
+
+export function CrossoverPanel({
+  pathAResult,
+  pathBResult,
+  isDark,
+  expertMode,
+  onExpertModeChange,
+  onCrossoverReady,
+}: CrossoverPanelProps) {
+  const [viewMode, setViewMode] = useState<ViewMode>('list');
+
+  const {
+    pairs,
+    selectedPairs,
+    pairsByType,
+    crossTypeStats,
+    applyPairs,
+    togglePairSelection,
+    selectPairsByType,
+    selectAll,
+    clearPairs,
+  } = useAttributeCrossover();
+
+  // Generate pairs when both results are available
+  useEffect(() => {
+    if (pathAResult && pathBResult) {
+      applyPairs(pathAResult, pathBResult);
+    } else {
+      clearPairs();
+    }
+  }, [pathAResult, pathBResult, applyPairs, clearPairs]);
+
+  // Notify parent when selection changes
+  useEffect(() => {
+    onCrossoverReady?.(selectedPairs);
+  }, [selectedPairs, onCrossoverReady]);
+
+  // Render when no data
+  if (!pathAResult || !pathBResult) {
+    return (
+      <div style={{
+        display: 'flex',
+        justifyContent: 'center',
+        alignItems: 'center',
+        height: '100%',
+      }}>
+        <Empty
+          description={
+            <Space direction="vertical" align="center">
+              <Text>Complete both Path A and Path B analysis first</Text>
+              <Text type="secondary">
+                {!pathAResult && !pathBResult
+                  ? 'Neither path has been analyzed'
+                  : !pathAResult
+                    ? 'Path A has not been analyzed'
+                    : 'Path B has not been analyzed'}
+              </Text>
+            </Space>
+          }
+        />
+      </div>
+    );
+  }
+
+  // Generate cross type labels dynamically
+  const getCrossTypeLabel = (crossType: string): string => {
+    if (crossType.startsWith('same-')) {
+      const category = crossType.replace('same-', '');
+      return `Same Category: ${category}`;
+    }
+    if (crossType.startsWith('cross-')) {
+      const parts = crossType.replace('cross-', '').split('-');
+      if (parts.length >= 2) {
+        return `Cross: ${parts[0]} × ${parts.slice(1).join('-')}`;
+      }
+    }
+    return crossType;
+  };
+
+  const renderListView = () => {
+    const crossTypes = Object.keys(pairsByType);
+
+    if (crossTypes.length === 0) {
+      return <Empty description="No crossover pairs generated" />;
+    }
+
+    const collapseItems = crossTypes.map(type => {
+      const typePairs = pairsByType[type];
+      const stats = crossTypeStats[type];
+      const label = getCrossTypeLabel(type);
+
+      return {
+        key: type,
+        label: (
+          <div style={{ display: 'flex', alignItems: 'center', gap: 8 }}>
+            <Checkbox
+              checked={stats.selected === stats.total}
+              indeterminate={stats.selected > 0 && stats.selected < stats.total}
+              onClick={(e) => e.stopPropagation()}
+              onChange={(e) => selectPairsByType(type, e.target.checked)}
+            />
+            <Text>{label}</Text>
+            <Badge
+              count={`${stats.selected}/${stats.total}`}
+              style={{
+                backgroundColor: stats.selected > 0 ? '#52c41a' : '#d9d9d9',
+              }}
+            />
+          </div>
+        ),
+        children: (
+          <div style={{
+            display: 'grid',
+            gridTemplateColumns: 'repeat(auto-fill, minmax(280px, 1fr))',
+            gap: 8,
+          }}>
+            {typePairs.map(pair => (
+              <CrossoverCard
+                key={pair.id}
+                pair={pair}
+                onToggle={togglePairSelection}
+                isDark={isDark}
+              />
+            ))}
+          </div>
+        ),
+      };
+    });
+
+    return (
+      <Collapse
+        items={collapseItems}
+        defaultActiveKey={crossTypes.filter(t => t.startsWith('same-'))}
+      />
+    );
+  };
+
+  const renderMatrixView = () => {
+    return (
+      <CrossoverMatrix
+        dagA={pathAResult}
+        dagB={pathBResult}
+        pairs={pairs}
+        onTogglePair={togglePairSelection}
+        isDark={isDark}
+      />
+    );
+  };
+
+  return (
+    <div style={{ height: '100%', display: 'flex', flexDirection: 'column' }}>
+      {/* Statistics Header */}
+      <Card size="small" style={{ marginBottom: 16 }}>
+        <Row gutter={16}>
+          <Col span={6}>
+            <Statistic
+              title="Total Pairs"
+              value={pairs.length}
+              prefix={<SwapOutlined />}
+            />
+          </Col>
+          <Col span={6}>
+            <Statistic
+              title="Selected"
+              value={selectedPairs.length}
+              prefix={<CheckCircleOutlined />}
+              valueStyle={{ color: '#52c41a' }}
+            />
+          </Col>
+          <Col span={6}>
+            <Statistic
+              title="Path A Attrs"
+              value={pathAResult.nodes.length}
+            />
+          </Col>
+          <Col span={6}>
+            <Statistic
+              title="Path B Attrs"
+              value={pathBResult.nodes.length}
+            />
+          </Col>
+        </Row>
+      </Card>
+
+      {/* Selection Preview */}
+      <CrossoverPreview
+        selectedPairs={selectedPairs}
+        dagA={pathAResult}
+        dagB={pathBResult}
+        isDark={isDark}
+      />
+
+      {/* Expert Mode Selection */}
+      <Card size="small" style={{ marginBottom: 16 }}>
+        <Space direction="vertical" style={{ width: '100%' }}>
+          <Text strong>Expert Team Mode</Text>
+          <Radio.Group
+            value={expertMode}
+            onChange={(e) => onExpertModeChange(e.target.value)}
+            buttonStyle="solid"
+          >
+            <Radio.Button value="shared">
+              Shared Experts
+            </Radio.Button>
+            <Radio.Button value="independent">
+              Independent Experts
+            </Radio.Button>
+          </Radio.Group>
+          <Text type="secondary" style={{ fontSize: 12 }}>
+            {expertMode === 'shared'
+              ? 'Both paths use the same expert team for crossover transformation'
+              : 'Each path uses its own expert team, combined for crossover'}
+          </Text>
+        </Space>
+      </Card>
+
+      {/* Actions */}
+      <div style={{ marginBottom: 16, display: 'flex', gap: 8 }}>
+        <Button
+          icon={<CheckCircleOutlined />}
+          onClick={() => selectAll(true)}
+        >
+          Select All
+        </Button>
+        <Button
+          onClick={() => selectAll(false)}
+        >
+          Deselect All
+        </Button>
+        <Button
+          icon={<ReloadOutlined />}
+          onClick={() => applyPairs(pathAResult, pathBResult)}
+        >
+          Regenerate
+        </Button>
+        <div style={{ flex: 1 }} />
+        <Radio.Group
+          value={viewMode}
+          onChange={(e) => setViewMode(e.target.value)}
+          buttonStyle="solid"
+          size="small"
+        >
+          <Radio.Button value="list">
+            <UnorderedListOutlined /> List
+          </Radio.Button>
+          <Radio.Button value="matrix">
+            <TableOutlined /> Matrix
+          </Radio.Button>
+        </Radio.Group>
+      </div>
+
+      {/* Content */}
+      <div style={{ flex: 1, overflow: 'auto' }}>
+        {viewMode === 'list' ? renderListView() : renderMatrixView()}
+      </div>
+    </div>
+  );
+}

frontend/src/components/DeduplicationPanel.tsx

@@ -26,6 +26,7 @@ import type {
   ExpertTransformationDAGResult,
   ExpertTransformationDescription,
   DeduplicationMethod,
+  PromptLanguage,
 } from '../types';
 
 const { Title, Text } = Typography;
@@ -37,6 +38,7 @@ interface DeduplicationPanelProps {
   onThresholdChange: (value: number) => void;
   method: DeduplicationMethod;
   onMethodChange?: (method: DeduplicationMethod) => void;  // Optional, handled in App.tsx sidebar
+  lang?: PromptLanguage;
 }
 
 /**
@@ -48,6 +50,7 @@ export const DeduplicationPanel: React.FC<DeduplicationPanelProps> = ({
   threshold,
   onThresholdChange,
   method,
+  lang = 'zh',
   // onMethodChange is handled in App.tsx sidebar
 }) => {
   const { loading, result, error, progress, deduplicate, clearResult } = useDeduplication();
@@ -70,7 +73,7 @@ export const DeduplicationPanel: React.FC<DeduplicationPanelProps> = ({
 
   const handleDeduplicate = () => {
     if (allDescriptions.length > 0) {
-      deduplicate(allDescriptions, threshold, method);
+      deduplicate(allDescriptions, threshold, method, lang);
     }
   };
 

frontend/src/components/DualPathInputPanel.tsx

@@ -0,0 +1,312 @@
+import { useState, useEffect } from 'react';
+import {
+  Input,
+  Button,
+  Select,
+  Typography,
+  Space,
+  message,
+  Slider,
+  Collapse,
+  Progress,
+  Card,
+  Alert,
+  Tag,
+  Divider,
+} from 'antd';
+import {
+  SearchOutlined,
+  LoadingOutlined,
+  SwapOutlined,
+} from '@ant-design/icons';
+import type { CategoryMode, DAGProgress, PromptLanguage } from '../types';
+import { getModels } from '../services/api';
+import { CategorySelector } from './CategorySelector';
+
+const { TextArea } = Input;
+const { Text } = Typography;
+
+interface DualPathInputPanelProps {
+  onAnalyze: (queryA: string, queryB: string, options?: {
+    model?: string;
+    temperature?: number;
+    chainCount?: number;
+    categoryMode?: CategoryMode;
+    customCategories?: string[];
+    suggestedCategoryCount?: number;
+    lang?: PromptLanguage;
+  }) => Promise<void>;
+  loadingA: boolean;
+  loadingB: boolean;
+  progressA: DAGProgress;
+  progressB: DAGProgress;
+  availableModels?: string[];
+  lang?: PromptLanguage;
+}
+
+export function DualPathInputPanel({
+  onAnalyze,
+  loadingA,
+  loadingB,
+  progressA,
+  progressB,
+  availableModels: propModels,
+  lang = 'zh',
+}: DualPathInputPanelProps) {
+  const [queryA, setQueryA] = useState('');
+  const [queryB, setQueryB] = useState('');
+  const [models, setModels] = useState<string[]>(propModels || []);
+  const [selectedModel, setSelectedModel] = useState<string | undefined>();
+  const [loadingModels, setLoadingModels] = useState(false);
+  const [temperature, setTemperature] = useState(0.7);
+  const [chainCount, setChainCount] = useState(5);
+  // Category settings
+  const [categoryMode, setCategoryMode] = useState<CategoryMode>('dynamic_auto' as CategoryMode);
+  const [customCategories, setCustomCategories] = useState<string[]>([]);
+  const [suggestedCategoryCount, setSuggestedCategoryCount] = useState(3);
+
+  const isLoading = loadingA || loadingB;
+
+  useEffect(() => {
+    if (propModels && propModels.length > 0) {
+      setModels(propModels);
+      if (!selectedModel) {
+        const defaultModel = propModels.find((m) => m.includes('qwen3')) || propModels[0];
+        setSelectedModel(defaultModel);
+      }
+      return;
+    }
+
+    async function fetchModels() {
+      setLoadingModels(true);
+      try {
+        const response = await getModels();
+        setModels(response.models);
+        if (response.models.length > 0 && !selectedModel) {
+          const defaultModel = response.models.find((m) => m.includes('qwen3')) || response.models[0];
+          setSelectedModel(defaultModel);
+        }
+      } catch {
+        message.error('Failed to fetch models');
+      } finally {
+        setLoadingModels(false);
+      }
+    }
+
+    fetchModels();
+  }, [propModels]);
+
+  const handleAnalyze = async () => {
+    if (!queryA.trim() || !queryB.trim()) {
+      message.warning(lang === 'zh' ? '請輸入兩個路徑的查詢內容' : 'Please enter queries for both paths');
+      return;
+    }
+
+    try {
+      await onAnalyze(queryA.trim(), queryB.trim(), {
+        model: selectedModel,
+        temperature,
+        chainCount,
+        categoryMode,
+        customCategories: customCategories.length > 0 ? customCategories : undefined,
+        suggestedCategoryCount,
+        lang,
+      });
+    } catch {
+      message.error(lang === 'zh' ? '分析失敗' : 'Analysis failed');
+    }
+  };
+
+  const handleSwapQueries = () => {
+    const temp = queryA;
+    setQueryA(queryB);
+    setQueryB(temp);
+  };
+
+  const renderProgressIndicator = (label: string, progress: DAGProgress, loading: boolean) => {
+    if (progress.step === 'idle' && !loading) return null;
+    if (progress.step === 'done') return null;
+
+    const percent = progress.step === 'step0'
+      ? 15
+      : progress.step === 'step1'
+        ? 50
+        : progress.step === 'relationships'
+          ? 85
+          : 100;
+
+    return (
+      <div style={{ marginTop: 8 }}>
+        <Text type="secondary" style={{ fontSize: 12 }}>{label}: {progress.message}</Text>
+        <Progress
+          percent={Math.round(percent)}
+          size="small"
+          status={progress.step === 'error' ? 'exception' : 'active'}
+          strokeColor={{ from: '#108ee9', to: '#87d068' }}
+        />
+      </div>
+    );
+  };
+
+  const collapseItems = [
+    {
+      key: 'categories',
+      label: 'Category Settings',
+      children: (
+        <CategorySelector
+          mode={categoryMode}
+          onModeChange={setCategoryMode}
+          customCategories={customCategories}
+          onCustomCategoriesChange={setCustomCategories}
+          suggestedCount={suggestedCategoryCount}
+          onSuggestedCountChange={setSuggestedCategoryCount}
+          disabled={isLoading}
+        />
+      ),
+    },
+    {
+      key: 'llm',
+      label: 'LLM Parameters',
+      children: (
+        <Space direction="vertical" style={{ width: '100%' }} size="middle">
+          <div>
+            <Text type="secondary" style={{ fontSize: 12 }}>Temperature: {temperature}</Text>
+            <Slider
+              min={0}
+              max={1}
+              step={0.1}
+              value={temperature}
+              onChange={setTemperature}
+              marks={{ 0: '0', 0.5: '0.5', 1: '1' }}
+              disabled={isLoading}
+            />
+          </div>
+          <div>
+            <Text type="secondary" style={{ fontSize: 12 }}>Chain Count: {chainCount}</Text>
+            <Slider
+              min={1}
+              max={10}
+              step={1}
+              value={chainCount}
+              onChange={setChainCount}
+              marks={{ 1: '1', 5: '5', 10: '10' }}
+              disabled={isLoading}
+            />
+          </div>
+        </Space>
+      ),
+    },
+  ];
+
+  return (
+    <div style={{
+      display: 'flex',
+      flexDirection: 'column',
+      height: '100%',
+      padding: 16,
+      gap: 16,
+    }}>
+      {/* Dual Path Input Card */}
+      <Card
+        size="small"
+        title={<Text strong>Dual Path Analysis</Text>}
+        styles={{ body: { padding: 12 } }}
+      >
+        <Space direction="vertical" style={{ width: '100%' }} size="middle">
+          {/* Model Selection */}
+          <Select
+            style={{ width: '100%' }}
+            value={selectedModel}
+            onChange={setSelectedModel}
+            loading={loadingModels}
+            placeholder="Select a model"
+            options={models.map((m) => ({ label: m, value: m }))}
+            size="middle"
+            disabled={isLoading}
+          />
+
+          {/* Path A Input */}
+          <div>
+            <Tag color="blue" style={{ marginBottom: 4 }}>Path A</Tag>
+            <TextArea
+              value={queryA}
+              onChange={(e) => setQueryA(e.target.value)}
+              placeholder="Enter first object (e.g., umbrella)"
+              autoSize={{ minRows: 1, maxRows: 2 }}
+              disabled={isLoading}
+            />
+            {renderProgressIndicator('Path A', progressA, loadingA)}
+          </div>
+
+          {/* Swap Button */}
+          <div style={{ textAlign: 'center' }}>
+            <Button
+              icon={<SwapOutlined rotate={90} />}
+              size="small"
+              onClick={handleSwapQueries}
+              disabled={isLoading}
+            >
+              Swap
+            </Button>
+          </div>
+
+          {/* Path B Input */}
+          <div>
+            <Tag color="green" style={{ marginBottom: 4 }}>Path B</Tag>
+            <TextArea
+              value={queryB}
+              onChange={(e) => setQueryB(e.target.value)}
+              placeholder="Enter second object (e.g., bicycle)"
+              autoSize={{ minRows: 1, maxRows: 2 }}
+              disabled={isLoading}
+            />
+            {renderProgressIndicator('Path B', progressB, loadingB)}
+          </div>
+
+          {/* Analyze Button */}
+          <Button
+            type="primary"
+            icon={<SearchOutlined />}
+            onClick={handleAnalyze}
+            loading={isLoading}
+            block
+            size="large"
+            disabled={!queryA.trim() || !queryB.trim()}
+          >
+            {isLoading ? 'Analyzing...' : 'Analyze Both'}
+          </Button>
+        </Space>
+      </Card>
+
+      {/* Combined Progress Alert */}
+      {isLoading && (
+        <Alert
+          type="info"
+          icon={<LoadingOutlined spin />}
+          message="Parallel Analysis in Progress"
+          description={
+            <Space direction="vertical" style={{ width: '100%' }}>
+              <div>
+                <Tag color="blue">A</Tag> {progressA.message || 'Waiting...'}
+              </div>
+              <div>
+                <Tag color="green">B</Tag> {progressB.message || 'Waiting...'}
+              </div>
+            </Space>
+          }
+          showIcon
+        />
+      )}
+
+      <Divider style={{ margin: '4px 0' }} />
+
+      {/* Settings Collapse */}
+      <Collapse
+        items={collapseItems}
+        defaultActiveKey={[]}
+        size="small"
+        style={{ background: 'transparent' }}
+      />
+    </div>
+  );
+}

frontend/src/components/DualPathMindmapPanel.tsx

@@ -0,0 +1,191 @@
+import { Empty, Spin, Tag, Typography } from 'antd';
+import type { PathState } from '../types';
+import { MindmapDAG } from './MindmapDAG';
+
+const { Text } = Typography;
+
+interface VisualSettings {
+  nodeSpacing: number;
+  fontSize: number;
+}
+
+interface DualPathMindmapPanelProps {
+  pathA: PathState;
+  pathB: PathState;
+  isDark: boolean;
+  visualSettings: VisualSettings;
+}
+
+interface SinglePathViewProps {
+  path: PathState;
+  label: string;
+  color: 'blue' | 'green';
+  isDark: boolean;
+  visualSettings: VisualSettings;
+}
+
+function SinglePathView({ path, label, color, isDark, visualSettings }: SinglePathViewProps) {
+  const { result, loading, error, query, progress } = path;
+
+  // Header with label
+  const headerStyle: React.CSSProperties = {
+    padding: '6px 12px',
+    background: isDark ? '#1f1f1f' : '#fafafa',
+    borderBottom: `1px solid ${isDark ? '#303030' : '#f0f0f0'}`,
+    display: 'flex',
+    alignItems: 'center',
+    gap: 8,
+    minHeight: 36,
+  };
+
+  const contentStyle: React.CSSProperties = {
+    flex: 1,
+    position: 'relative',
+    overflow: 'hidden',
+  };
+
+  const renderContent = () => {
+    if (loading) {
+      return (
+        <div style={{
+          display: 'flex',
+          flexDirection: 'column',
+          justifyContent: 'center',
+          alignItems: 'center',
+          height: '100%',
+          gap: 8,
+        }}>
+          <Spin size="large" />
+          <Text type="secondary">{progress.message || 'Analyzing...'}</Text>
+        </div>
+      );
+    }
+
+    if (error) {
+      return (
+        <div style={{
+          display: 'flex',
+          justifyContent: 'center',
+          alignItems: 'center',
+          height: '100%',
+        }}>
+          <Empty description={error} />
+        </div>
+      );
+    }
+
+    if (!result) {
+      return (
+        <div style={{
+          display: 'flex',
+          justifyContent: 'center',
+          alignItems: 'center',
+          height: '100%',
+        }}>
+          <Empty description={`Enter a query for ${label}`} />
+        </div>
+      );
+    }
+
+    return (
+      <MindmapDAG
+        data={result}
+        isDark={isDark}
+        visualSettings={visualSettings}
+      />
+    );
+  };
+
+  return (
+    <div style={{ display: 'flex', flexDirection: 'column', height: '100%' }}>
+      <div style={headerStyle}>
+        <Tag color={color}>{label}</Tag>
+        {result && (
+          <Text strong style={{ flex: 1 }}>
+            {result.query}
+          </Text>
+        )}
+        {!result && query && (
+          <Text type="secondary" style={{ flex: 1 }}>
+            {query}
+          </Text>
+        )}
+        {result && (
+          <Text type="secondary" style={{ fontSize: 12 }}>
+            {result.nodes.length} attributes
+          </Text>
+        )}
+      </div>
+      <div style={contentStyle}>
+        {renderContent()}
+      </div>
+    </div>
+  );
+}
+
+export function DualPathMindmapPanel({
+  pathA,
+  pathB,
+  isDark,
+  visualSettings,
+}: DualPathMindmapPanelProps) {
+  const containerStyle: React.CSSProperties = {
+    display: 'flex',
+    flexDirection: 'column',
+    height: '100%',
+    gap: 2,
+  };
+
+  const pathContainerStyle: React.CSSProperties = {
+    flex: 1,
+    minHeight: 0,
+    borderRadius: 6,
+    overflow: 'hidden',
+    border: `1px solid ${isDark ? '#303030' : '#f0f0f0'}`,
+  };
+
+  const dividerStyle: React.CSSProperties = {
+    height: 4,
+    background: isDark ? '#303030' : '#f0f0f0',
+    cursor: 'row-resize',
+    display: 'flex',
+    alignItems: 'center',
+    justifyContent: 'center',
+  };
+
+  return (
+    <div style={containerStyle}>
+      {/* Path A - Top Half */}
+      <div style={pathContainerStyle}>
+        <SinglePathView
+          path={pathA}
+          label="Path A"
+          color="blue"
+          isDark={isDark}
+          visualSettings={visualSettings}
+        />
+      </div>
+
+      {/* Divider */}
+      <div style={dividerStyle}>
+        <div style={{
+          width: 40,
+          height: 3,
+          borderRadius: 2,
+          background: isDark ? '#505050' : '#d0d0d0',
+        }} />
+      </div>
+
+      {/* Path B - Bottom Half */}
+      <div style={pathContainerStyle}>
+        <SinglePathView
+          path={pathB}
+          label="Path B"
+          color="green"
+          isDark={isDark}
+          visualSettings={visualSettings}
+        />
+      </div>
+    </div>
+  );
+}

frontend/src/components/DualTransformationPanel.tsx

@@ -0,0 +1,356 @@
+import { useCallback, useEffect, useMemo, useState } from 'react';
+import { Empty, Spin, Card, Space, Typography, Tag, Button, Progress } from 'antd';
+import { ThunderboltOutlined, ReloadOutlined } from '@ant-design/icons';
+import type { AttributeDAG, ExpertSource, ExpertTransformationDAGResult, PromptLanguage } from '../types';
+import { TransformationDAG } from './TransformationDAG';
+import { useExpertTransformation } from '../hooks/useExpertTransformation';
+
+const { Text } = Typography;
+
+interface DualTransformationPanelProps {
+  crossoverDAGA: AttributeDAG | null;  // Path A with attributes from B
+  crossoverDAGB: AttributeDAG | null;  // Path B with attributes from A
+  isDark: boolean;
+  model?: string;
+  temperature?: number;
+  expertConfig: {
+    expert_count: number;
+    keywords_per_expert: number;
+    custom_experts?: string[];
+  };
+  expertSource: ExpertSource;
+  expertLanguage: 'en' | 'zh';
+  lang?: PromptLanguage;
+  shouldStartTransform: boolean;
+  onTransformComplete: () => void;
+  onLoadingChange: (loading: boolean) => void;
+  onResultsChange?: (results: { pathA: ExpertTransformationDAGResult | null; pathB: ExpertTransformationDAGResult | null }) => void;
+}
+
+interface SinglePathTransformProps {
+  label: string;
+  color: 'blue' | 'green';
+  attributeData: AttributeDAG | null;
+  crossoverSource: string; // The query from which attributes were crossed over
+  isDark: boolean;
+  model?: string;
+  temperature?: number;
+  expertConfig: {
+    expert_count: number;
+    keywords_per_expert: number;
+    custom_experts?: string[];
+  };
+  expertSource: ExpertSource;
+  expertLanguage: 'en' | 'zh';
+  lang?: PromptLanguage;
+  shouldStart: boolean;
+  onComplete: () => void;
+  onResultChange: (result: ExpertTransformationDAGResult | null) => void;
+}
+
+function SinglePathTransform({
+  label,
+  color,
+  attributeData,
+  crossoverSource,
+  isDark,
+  model,
+  temperature,
+  expertConfig,
+  expertSource,
+  expertLanguage,
+  lang = 'zh',
+  shouldStart,
+  onComplete,
+  onResultChange,
+}: SinglePathTransformProps) {
+  const {
+    loading,
+    progress,
+    results,
+    transformAll,
+    clearResults,
+  } = useExpertTransformation({ model, temperature, expertSource, expertLanguage, lang });
+
+  // Notify parent of results changes
+  useEffect(() => {
+    onResultChange(results);
+  }, [results, onResultChange]);
+
+  // Build transformation input
+  const transformationInput = useMemo(() => {
+    if (!attributeData) return null;
+
+    const attributesByCategory: Record<string, string[]> = {};
+    for (const node of attributeData.nodes) {
+      if (!attributesByCategory[node.category]) {
+        attributesByCategory[node.category] = [];
+      }
+      attributesByCategory[node.category].push(node.name);
+    }
+
+    return {
+      query: attributeData.query,
+      categories: attributeData.categories,
+      attributesByCategory,
+      expertConfig,
+    };
+  }, [attributeData, expertConfig]);
+
+  // Handle transform trigger
+  useEffect(() => {
+    if (shouldStart && transformationInput && !loading && !results) {
+      transformAll(transformationInput).then(() => onComplete());
+    }
+  }, [shouldStart, transformationInput, loading, results, transformAll, onComplete]);
+
+  // Progress percentage
+  const progressPercent = useMemo(() => {
+    if (!transformationInput || progress.step === 'idle') return 0;
+    const totalCategories = transformationInput.categories.length;
+    if (totalCategories === 0) return 0;
+    const completed = progress.processedCategories.length;
+    return Math.round((completed / totalCategories) * 100);
+  }, [transformationInput, progress]);
+
+  const headerStyle: React.CSSProperties = {
+    padding: '8px 12px',
+    background: isDark ? '#1f1f1f' : '#fafafa',
+    borderBottom: `1px solid ${isDark ? '#303030' : '#f0f0f0'}`,
+    display: 'flex',
+    alignItems: 'center',
+    gap: 8,
+  };
+
+  if (!attributeData || attributeData.nodes.length === 0) {
+    return (
+      <div style={{ height: '100%', display: 'flex', flexDirection: 'column' }}>
+        <div style={headerStyle}>
+          <Tag color={color}>{label}</Tag>
+          <Text type="secondary">No crossover attributes selected</Text>
+        </div>
+        <div style={{ flex: 1, display: 'flex', justifyContent: 'center', alignItems: 'center' }}>
+          <Empty description="Select crossover pairs first" />
+        </div>
+      </div>
+    );
+  }
+
+  if (loading) {
+    return (
+      <div style={{ height: '100%', display: 'flex', flexDirection: 'column' }}>
+        <div style={headerStyle}>
+          <Tag color={color}>{label}</Tag>
+          <Text strong>{attributeData.query}</Text>
+          <Text type="secondary">← receiving {attributeData.nodes.length} attributes</Text>
+        </div>
+        <div style={{ flex: 1, display: 'flex', flexDirection: 'column', justifyContent: 'center', alignItems: 'center', gap: 16 }}>
+          <Spin size="large" />
+          <Text>{progress.message}</Text>
+          <Progress percent={progressPercent} style={{ width: 200 }} />
+        </div>
+      </div>
+    );
+  }
+
+  if (results) {
+    return (
+      <div style={{ height: '100%', display: 'flex', flexDirection: 'column' }}>
+        <div style={headerStyle}>
+          <Tag color={color}>{label}</Tag>
+          <Text strong>{attributeData.query}</Text>
+          <Text type="secondary">
+            {results.results.reduce((sum, r) => sum + r.descriptions.length, 0)} descriptions
+          </Text>
+          <div style={{ flex: 1 }} />
+          <Button size="small" icon={<ReloadOutlined />} onClick={clearResults}>
+            Clear
+          </Button>
+        </div>
+        <div style={{ flex: 1, overflow: 'hidden' }}>
+          <TransformationDAG
+            data={results}
+            categories={attributeData.categories}
+            isDark={isDark}
+            crossoverSource={crossoverSource}
+          />
+        </div>
+      </div>
+    );
+  }
+
+  // Ready state - show attributes to transform
+  return (
+    <div style={{ height: '100%', display: 'flex', flexDirection: 'column' }}>
+      <div style={headerStyle}>
+        <Tag color={color}>{label}</Tag>
+        <Text strong>{attributeData.query}</Text>
+        <Text type="secondary">← {attributeData.nodes.length} attributes to transform</Text>
+      </div>
+      <div style={{ flex: 1, padding: 16, overflow: 'auto' }}>
+        <Card size="small" title="Crossover Attributes">
+          <Space wrap>
+            {attributeData.nodes.map(node => (
+              <Tag key={node.id} color={color === 'blue' ? 'green' : 'blue'}>
+                {node.name}
+                <Text type="secondary" style={{ marginLeft: 4, fontSize: 10 }}>
+                  ({node.category})
+                </Text>
+              </Tag>
+            ))}
+          </Space>
+        </Card>
+        <Text type="secondary" style={{ display: 'block', marginTop: 12, fontSize: 12 }}>
+          These attributes will be transformed with "{attributeData.query}" as context.
+        </Text>
+      </div>
+    </div>
+  );
+}
+
+export function DualTransformationPanel({
+  crossoverDAGA,
+  crossoverDAGB,
+  isDark,
+  model,
+  temperature,
+  expertConfig,
+  expertSource,
+  expertLanguage,
+  lang = 'zh',
+  shouldStartTransform,
+  onTransformComplete,
+  onLoadingChange,
+  onResultsChange,
+}: DualTransformationPanelProps) {
+  const [resultA, setResultA] = useState<ExpertTransformationDAGResult | null>(null);
+  const [resultB, setResultB] = useState<ExpertTransformationDAGResult | null>(null);
+  const [triggerA, setTriggerA] = useState(false);
+  const [triggerB, setTriggerB] = useState(false);
+
+  // Handle external transform trigger
+  useEffect(() => {
+    if (shouldStartTransform) {
+      setTriggerA(true);
+      setTriggerB(true);
+      onLoadingChange(true);
+    }
+  }, [shouldStartTransform, onLoadingChange]);
+
+  // Notify parent of results
+  useEffect(() => {
+    onResultsChange?.({ pathA: resultA, pathB: resultB });
+  }, [resultA, resultB, onResultsChange]);
+
+  const handleCompleteA = useCallback(() => {
+    setTriggerA(false);
+  }, []);
+
+  const handleCompleteB = useCallback(() => {
+    setTriggerB(false);
+  }, []);
+
+  // When both are done, notify parent
+  useEffect(() => {
+    if (!triggerA && !triggerB && (resultA || resultB)) {
+      onLoadingChange(false);
+      onTransformComplete();
+    }
+  }, [triggerA, triggerB, resultA, resultB, onLoadingChange, onTransformComplete]);
+
+  const containerStyle: React.CSSProperties = {
+    display: 'flex',
+    flexDirection: 'column',
+    height: '100%',
+    gap: 2,
+  };
+
+  const pathContainerStyle: React.CSSProperties = {
+    flex: 1,
+    minHeight: 0,
+    borderRadius: 6,
+    overflow: 'hidden',
+    border: `1px solid ${isDark ? '#303030' : '#f0f0f0'}`,
+  };
+
+  const dividerStyle: React.CSSProperties = {
+    height: 4,
+    background: isDark ? '#303030' : '#f0f0f0',
+    display: 'flex',
+    alignItems: 'center',
+    justifyContent: 'center',
+  };
+
+  // Show empty state if no crossover data
+  if (!crossoverDAGA && !crossoverDAGB) {
+    return (
+      <div style={{ height: '100%', display: 'flex', justifyContent: 'center', alignItems: 'center' }}>
+        <Empty
+          description={
+            <Space direction="vertical" align="center">
+              <ThunderboltOutlined style={{ fontSize: 32, color: isDark ? '#444' : '#ccc' }} />
+              <Text>Select crossover pairs first</Text>
+              <Text type="secondary">
+                Go to the Crossover tab and select attribute pairs to transform
+              </Text>
+            </Space>
+          }
+        />
+      </div>
+    );
+  }
+
+  return (
+    <div style={containerStyle}>
+      {/* Path A - Top (receives attributes from Path B) */}
+      <div style={pathContainerStyle}>
+        <SinglePathTransform
+          label="Path A"
+          color="blue"
+          attributeData={crossoverDAGA}
+          crossoverSource={crossoverDAGB?.query || 'Path B'}
+          isDark={isDark}
+          model={model}
+          temperature={temperature}
+          expertConfig={expertConfig}
+          expertSource={expertSource}
+          expertLanguage={expertLanguage}
+          lang={lang}
+          shouldStart={triggerA}
+          onComplete={handleCompleteA}
+          onResultChange={setResultA}
+        />
+      </div>
+
+      {/* Divider */}
+      <div style={dividerStyle}>
+        <div style={{
+          width: 40,
+          height: 3,
+          borderRadius: 2,
+          background: isDark ? '#505050' : '#d0d0d0',
+        }} />
+      </div>
+
+      {/* Path B - Bottom (receives attributes from Path A) */}
+      <div style={pathContainerStyle}>
+        <SinglePathTransform
+          label="Path B"
+          color="green"
+          attributeData={crossoverDAGB}
+          crossoverSource={crossoverDAGA?.query || 'Path A'}
+          isDark={isDark}
+          model={model}
+          temperature={temperature}
+          expertConfig={expertConfig}
+          expertSource={expertSource}
+          expertLanguage={expertLanguage}
+          lang={lang}
+          shouldStart={triggerB}
+          onComplete={handleCompleteB}
+          onResultChange={setResultB}
+        />
+      </div>
+    </div>
+  );
+}

frontend/src/components/InputPanel.tsx

@@ -23,7 +23,7 @@ import {
   FileTextOutlined,
   CodeOutlined,
 } from '@ant-design/icons';
-import type { AttributeDAG, CategoryMode } from '../types';
+import type { AttributeDAG, CategoryMode, PromptLanguage } from '../types';
 import { getModels } from '../services/api';
 import { CategorySelector } from './CategorySelector';
 
@@ -59,12 +59,14 @@ interface InputPanelProps {
     chainCount?: number,
     categoryMode?: CategoryMode,
     customCategories?: string[],
-    suggestedCategoryCount?: number
+    suggestedCategoryCount?: number,
+    lang?: PromptLanguage
   ) => Promise<void>;
-  onLoadHistory: (item: DAGHistoryItem) => void;
+  onLoadHistory: (item: DAGHistoryItem, lang?: PromptLanguage) => void;
   onResetView?: () => void;
   visualSettings: VisualSettings;
   onVisualSettingsChange: (settings: VisualSettings) => void;
+  lang?: PromptLanguage;
 }
 
 export function InputPanel({
@@ -77,6 +79,7 @@ export function InputPanel({
   onResetView,
   visualSettings,
   onVisualSettingsChange,
+  lang = 'zh',
 }: InputPanelProps) {
   const [query, setQuery] = useState('');
   const [models, setModels] = useState<string[]>([]);
@@ -111,7 +114,7 @@ export function InputPanel({
 
   const handleAnalyze = async () => {
     if (!query.trim()) {
-      message.warning('Please enter a query');
+      message.warning(lang === 'zh' ? '請輸入查詢內容' : 'Please enter a query');
       return;
     }
 
@@ -123,11 +126,12 @@ export function InputPanel({
         chainCount,
         categoryMode,
         customCategories.length > 0 ? customCategories : undefined,
-        suggestedCategoryCount
+        suggestedCategoryCount,
+        lang
       );
       setQuery('');
     } catch {
-      message.error('Analysis failed');
+      message.error(lang === 'zh' ? '分析失敗' : 'Analysis failed');
     }
   };
 
@@ -489,7 +493,7 @@ export function InputPanel({
                 marginBottom: 4,
                 transition: 'background 0.2s',
               }}
-              onClick={() => onLoadHistory(item)}
+              onClick={() => onLoadHistory(item, lang)}
               className="history-item"
             >
               <div style={{ flex: 1, minWidth: 0 }}>

frontend/src/components/PatentSearchPanel.tsx

@@ -0,0 +1,344 @@
+import { useState, useCallback } from 'react';
+import {
+  Card,
+  Button,
+  Space,
+  Typography,
+  Empty,
+  Input,
+  Tag,
+  List,
+  Tooltip,
+  message,
+} from 'antd';
+import {
+  SearchOutlined,
+  LinkOutlined,
+  CopyOutlined,
+  DeleteOutlined,
+  GlobalOutlined,
+} from '@ant-design/icons';
+import type {
+  ExpertTransformationDescription,
+} from '../types';
+
+const { Text, Paragraph } = Typography;
+const { TextArea } = Input;
+
+interface PatentSearchPanelProps {
+  descriptions?: ExpertTransformationDescription[];
+  isDark: boolean;
+}
+
+interface SearchItem {
+  id: string;
+  query: string;
+  searchUrl: string;
+  expertName?: string;
+  keyword?: string;
+}
+
+// Generate Google Patents search URL
+function generatePatentSearchUrl(query: string): string {
+  // Extract key terms and create a search-friendly query
+  const encodedQuery = encodeURIComponent(query);
+  return `https://patents.google.com/?q=${encodedQuery}`;
+}
+
+// Generate Lens.org search URL (alternative)
+function generateLensSearchUrl(query: string): string {
+  const encodedQuery = encodeURIComponent(query);
+  return `https://www.lens.org/lens/search/patent/list?q=${encodedQuery}`;
+}
+
+export function PatentSearchPanel({ descriptions, isDark }: PatentSearchPanelProps) {
+  const [customQuery, setCustomQuery] = useState('');
+  const [searchItems, setSearchItems] = useState<SearchItem[]>([]);
+  const [selectedDescriptions, setSelectedDescriptions] = useState<Set<number>>(new Set());
+
+  // Add custom query to search list
+  const handleAddCustomQuery = useCallback(() => {
+    if (!customQuery.trim()) return;
+
+    const newItem: SearchItem = {
+      id: `custom-${Date.now()}`,
+      query: customQuery.trim(),
+      searchUrl: generatePatentSearchUrl(customQuery.trim()),
+    };
+
+    setSearchItems(prev => [newItem, ...prev]);
+    setCustomQuery('');
+    message.success('Added to search list');
+  }, [customQuery]);
+
+  // Add selected descriptions to search list
+  const handleAddSelected = useCallback(() => {
+    if (!descriptions || selectedDescriptions.size === 0) return;
+
+    const newItems: SearchItem[] = Array.from(selectedDescriptions).map(idx => {
+      const desc = descriptions[idx];
+      return {
+        id: `desc-${idx}-${Date.now()}`,
+        query: desc.description,
+        searchUrl: generatePatentSearchUrl(desc.description),
+        expertName: desc.expert_name,
+        keyword: desc.keyword,
+      };
+    });
+
+    setSearchItems(prev => [...newItems, ...prev]);
+    setSelectedDescriptions(new Set());
+    message.success(`Added ${newItems.length} items to search list`);
+  }, [descriptions, selectedDescriptions]);
+
+  // Remove item from list
+  const handleRemoveItem = useCallback((id: string) => {
+    setSearchItems(prev => prev.filter(item => item.id !== id));
+  }, []);
+
+  // Copy URL to clipboard
+  const handleCopyUrl = useCallback((url: string) => {
+    navigator.clipboard.writeText(url);
+    message.success('URL copied to clipboard');
+  }, []);
+
+  // Toggle description selection
+  const toggleDescription = useCallback((index: number) => {
+    setSelectedDescriptions(prev => {
+      const next = new Set(prev);
+      if (next.has(index)) {
+        next.delete(index);
+      } else {
+        next.add(index);
+      }
+      return next;
+    });
+  }, []);
+
+  // Clear all
+  const handleClearAll = useCallback(() => {
+    setSearchItems([]);
+  }, []);
+
+  const containerStyle: React.CSSProperties = {
+    height: '100%',
+    display: 'flex',
+    flexDirection: 'column',
+    gap: 16,
+    padding: 16,
+    overflow: 'auto',
+  };
+
+  const cardStyle: React.CSSProperties = {
+    background: isDark ? '#1f1f1f' : '#fff',
+  };
+
+  return (
+    <div style={containerStyle}>
+      {/* Info banner */}
+      <Card size="small" style={cardStyle}>
+        <Space>
+          <GlobalOutlined style={{ color: '#1890ff' }} />
+          <Text>
+            Generate search links to check for similar patents on Google Patents or Lens.org
+          </Text>
+        </Space>
+      </Card>
+
+      {/* Custom search input */}
+      <Card size="small" title="Add Custom Search" style={cardStyle}>
+        <TextArea
+          placeholder="Enter a description to search for similar patents..."
+          value={customQuery}
+          onChange={e => setCustomQuery(e.target.value)}
+          autoSize={{ minRows: 2, maxRows: 4 }}
+          style={{ marginBottom: 8 }}
+        />
+        <Button
+          type="primary"
+          icon={<SearchOutlined />}
+          onClick={handleAddCustomQuery}
+          disabled={!customQuery.trim()}
+        >
+          Add to Search List
+        </Button>
+      </Card>
+
+      {/* Description selection (if available) */}
+      {descriptions && descriptions.length > 0 && (
+        <Card
+          size="small"
+          title={`Generated Descriptions (${descriptions.length})`}
+          style={cardStyle}
+          extra={
+            <Button
+              type="primary"
+              size="small"
+              icon={<SearchOutlined />}
+              onClick={handleAddSelected}
+              disabled={selectedDescriptions.size === 0}
+            >
+              Add Selected ({selectedDescriptions.size})
+            </Button>
+          }
+        >
+          <div style={{ maxHeight: 200, overflow: 'auto' }}>
+            <Space direction="vertical" style={{ width: '100%' }}>
+              {descriptions.slice(0, 20).map((desc, idx) => (
+                <div
+                  key={idx}
+                  onClick={() => toggleDescription(idx)}
+                  style={{
+                    padding: 8,
+                    borderRadius: 4,
+                    cursor: 'pointer',
+                    background: selectedDescriptions.has(idx)
+                      ? (isDark ? '#177ddc22' : '#1890ff11')
+                      : (isDark ? '#141414' : '#fafafa'),
+                    border: selectedDescriptions.has(idx)
+                      ? `1px solid ${isDark ? '#177ddc' : '#1890ff'}`
+                      : `1px solid ${isDark ? '#303030' : '#f0f0f0'}`,
+                  }}
+                >
+                  <Space size={4}>
+                    <Tag color="blue" style={{ fontSize: 10 }}>{desc.expert_name}</Tag>
+                    <Tag style={{ fontSize: 10 }}>{desc.keyword}</Tag>
+                  </Space>
+                  <Paragraph
+                    ellipsis={{ rows: 2 }}
+                    style={{ marginBottom: 0, marginTop: 4, fontSize: 12 }}
+                  >
+                    {desc.description}
+                  </Paragraph>
+                </div>
+              ))}
+              {descriptions.length > 20 && (
+                <Text type="secondary">
+                  And {descriptions.length - 20} more descriptions...
+                </Text>
+              )}
+            </Space>
+          </div>
+        </Card>
+      )}
+
+      {/* Search list */}
+      {searchItems.length > 0 && (
+        <Card
+          size="small"
+          title={`Search List (${searchItems.length})`}
+          style={{ ...cardStyle, flex: 1, minHeight: 0, overflow: 'hidden', display: 'flex', flexDirection: 'column' }}
+          extra={
+            <Button size="small" danger onClick={handleClearAll}>
+              Clear All
+            </Button>
+          }
+          bodyStyle={{ flex: 1, overflow: 'auto', padding: 0 }}
+        >
+          <List
+            dataSource={searchItems}
+            renderItem={item => (
+              <List.Item
+                style={{
+                  padding: '12px 16px',
+                  borderBottom: `1px solid ${isDark ? '#303030' : '#f0f0f0'}`,
+                }}
+                actions={[
+                  <Tooltip title="Open in Google Patents" key="google">
+                    <Button
+                      type="link"
+                      icon={<LinkOutlined />}
+                      href={item.searchUrl}
+                      target="_blank"
+                    >
+                      Google
+                    </Button>
+                  </Tooltip>,
+                  <Tooltip title="Open in Lens.org" key="lens">
+                    <Button
+                      type="link"
+                      icon={<GlobalOutlined />}
+                      href={generateLensSearchUrl(item.query)}
+                      target="_blank"
+                    >
+                      Lens
+                    </Button>
+                  </Tooltip>,
+                  <Tooltip title="Copy URL" key="copy">
+                    <Button
+                      type="text"
+                      icon={<CopyOutlined />}
+                      onClick={() => handleCopyUrl(item.searchUrl)}
+                    />
+                  </Tooltip>,
+                  <Tooltip title="Remove" key="remove">
+                    <Button
+                      type="text"
+                      danger
+                      icon={<DeleteOutlined />}
+                      onClick={() => handleRemoveItem(item.id)}
+                    />
+                  </Tooltip>,
+                ]}
+              >
+                <List.Item.Meta
+                  title={
+                    <Space size={4}>
+                      {item.expertName && (
+                        <Tag color="blue" style={{ fontSize: 10 }}>{item.expertName}</Tag>
+                      )}
+                      {item.keyword && (
+                        <Tag style={{ fontSize: 10 }}>{item.keyword}</Tag>
+                      )}
+                    </Space>
+                  }
+                  description={
+                    <Paragraph
+                      ellipsis={{ rows: 2 }}
+                      style={{ marginBottom: 0, fontSize: 12 }}
+                    >
+                      {item.query}
+                    </Paragraph>
+                  }
+                />
+              </List.Item>
+            )}
+          />
+        </Card>
+      )}
+
+      {/* Empty state */}
+      {searchItems.length === 0 && (!descriptions || descriptions.length === 0) && (
+        <Card style={cardStyle}>
+          <Empty
+            description={
+              <Space direction="vertical">
+                <Text>Enter a description or run transformations first</Text>
+                <Text type="secondary">
+                  Search links will open in Google Patents or Lens.org
+                </Text>
+              </Space>
+            }
+          />
+        </Card>
+      )}
+
+      {/* Empty state with descriptions but no search items */}
+      {searchItems.length === 0 && descriptions && descriptions.length > 0 && (
+        <Card style={cardStyle}>
+          <Empty
+            image={Empty.PRESENTED_IMAGE_SIMPLE}
+            description={
+              <Space direction="vertical">
+                <Text>Select descriptions above to add to search list</Text>
+                <Text type="secondary">
+                  Then click the links to search on Google Patents or Lens.org
+                </Text>
+              </Space>
+            }
+          />
+        </Card>
+      )}
+    </div>
+  );
+}

frontend/src/components/TransformationDAG.tsx

@@ -23,6 +23,7 @@ interface TransformationDAGProps {
   data: TransformationDAGResult | ExpertTransformationDAGResult;
   categories: CategoryDefinition[];
   isDark: boolean;
+  crossoverSource?: string; // If set, marks all attributes as crossed over from this source
 }
 
 export interface TransformationDAGRef {
@@ -30,7 +31,7 @@ export interface TransformationDAGRef {
 }
 
 const TransformationDAGInner = forwardRef<TransformationDAGRef, TransformationDAGProps>(
-  ({ data, categories, isDark }, ref) => {
+  ({ data, categories, isDark, crossoverSource }, ref) => {
     const { setViewport } = useReactFlow();
 
     // Check if data is ExpertTransformationDAGResult by checking for 'experts' property
@@ -46,7 +47,7 @@ const TransformationDAGInner = forwardRef<TransformationDAGRef, TransformationDA
     const expertLayout = useExpertTransformationLayout(
       isExpertTransformation ? (data as ExpertTransformationDAGResult) : null,
       categories,
-      { isDark, fontSize: 13 }
+      { isDark, fontSize: 13, crossoverSource }
     );
 
     const { nodes, edges } = isExpertTransformation ? expertLayout : regularLayout;

frontend/src/components/TransformationPanel.tsx

@@ -1,7 +1,7 @@
 import { forwardRef, useMemo, useCallback, useEffect } from 'react';
 import { Empty, Spin, Button, Progress, Card, Space, Typography, Tag } from 'antd';
 import { ReloadOutlined } from '@ant-design/icons';
-import type { AttributeDAG, ExpertTransformationInput, ExpertSource, ExpertTransformationDAGResult } from '../types';
+import type { AttributeDAG, ExpertTransformationInput, ExpertSource, ExpertTransformationDAGResult, PromptLanguage } from '../types';
 import { TransformationDAG } from './TransformationDAG';
 import type { TransformationDAGRef } from './TransformationDAG';
 import { useExpertTransformation } from '../hooks/useExpertTransformation';
@@ -20,6 +20,7 @@ interface TransformationPanelProps {
   };
   expertSource: ExpertSource;
   expertLanguage: 'en' | 'zh';
+  lang?: PromptLanguage;
   shouldStartTransform: boolean;
   onTransformComplete: () => void;
   onLoadingChange: (loading: boolean) => void;
@@ -27,14 +28,14 @@ interface TransformationPanelProps {
 }
 
 export const TransformationPanel = forwardRef<TransformationDAGRef, TransformationPanelProps>(
-  ({ attributeData, isDark, model, temperature, expertConfig, expertSource, expertLanguage, shouldStartTransform, onTransformComplete, onLoadingChange, onResultsChange }, ref) => {
+  ({ attributeData, isDark, model, temperature, expertConfig, expertSource, expertLanguage, lang = 'zh', shouldStartTransform, onTransformComplete, onLoadingChange, onResultsChange }, ref) => {
     const {
       loading,
       progress,
       results,
       transformAll,
       clearResults,
-    } = useExpertTransformation({ model, temperature, expertSource, expertLanguage });
+    } = useExpertTransformation({ model, temperature, expertSource, expertLanguage, lang });
 
     // Notify parent of loading state changes
     useEffect(() => {

frontend/src/components/crossover/CrossoverCard.tsx

@@ -0,0 +1,76 @@
+import { Card, Tag, Checkbox, Typography, Space } from 'antd';
+import { SwapOutlined } from '@ant-design/icons';
+import type { CrossoverPair } from '../../types';
+
+const { Text } = Typography;
+
+interface CrossoverCardProps {
+  pair: CrossoverPair;
+  onToggle: (pairId: string) => void;
+  isDark: boolean;
+}
+
+export function CrossoverCard({ pair, onToggle, isDark }: CrossoverCardProps) {
+  const sourceColor = pair.sourcePathId === 'A' ? 'blue' : 'green';
+  const targetColor = pair.targetPathId === 'A' ? 'blue' : 'green';
+
+  return (
+    <Card
+      size="small"
+      hoverable
+      onClick={() => onToggle(pair.id)}
+      style={{
+        cursor: 'pointer',
+        border: pair.selected
+          ? `2px solid ${isDark ? '#1890ff' : '#1890ff'}`
+          : `1px solid ${isDark ? '#303030' : '#f0f0f0'}`,
+        background: pair.selected
+          ? (isDark ? '#111d2c' : '#e6f4ff')
+          : (isDark ? '#1f1f1f' : '#fff'),
+      }}
+      styles={{
+        body: { padding: '8px 12px' },
+      }}
+    >
+      <Space direction="vertical" size={4} style={{ width: '100%' }}>
+        {/* Header with checkbox */}
+        <div style={{ display: 'flex', alignItems: 'center', gap: 8 }}>
+          <Checkbox
+            checked={pair.selected}
+            onChange={() => onToggle(pair.id)}
+            onClick={(e) => e.stopPropagation()}
+          />
+          <Tag style={{ margin: 0, fontSize: 10 }}>{pair.crossType}</Tag>
+        </div>
+
+        {/* Crossover visualization */}
+        <div style={{ display: 'flex', alignItems: 'center', gap: 8 }}>
+          {/* Source */}
+          <div style={{ flex: 1, textAlign: 'right' }}>
+            <Tag color={sourceColor} style={{ margin: 0 }}>
+              {pair.sourcePathId}
+            </Tag>
+            <Text style={{ marginLeft: 4 }}>{pair.sourceNode.name}</Text>
+            <Text type="secondary" style={{ fontSize: 10, display: 'block' }}>
+              {pair.sourceNode.category}
+            </Text>
+          </div>
+
+          {/* Cross icon */}
+          <SwapOutlined style={{ color: isDark ? '#888' : '#999' }} />
+
+          {/* Target */}
+          <div style={{ flex: 1 }}>
+            <Tag color={targetColor} style={{ margin: 0 }}>
+              {pair.targetPathId}
+            </Tag>
+            <Text style={{ marginLeft: 4 }}>{pair.targetNode.name}</Text>
+            <Text type="secondary" style={{ fontSize: 10, display: 'block' }}>
+              {pair.targetNode.category}
+            </Text>
+          </div>
+        </div>
+      </Space>
+    </Card>
+  );
+}

frontend/src/components/crossover/CrossoverMatrix.tsx

@@ -0,0 +1,163 @@
+import { useMemo } from 'react';
+import { Table, Tag, Checkbox, Typography, Tooltip } from 'antd';
+import type { AttributeDAG, CrossoverPair, DAGNode } from '../../types';
+
+const { Text } = Typography;
+
+interface CrossoverMatrixProps {
+  dagA: AttributeDAG;
+  dagB: AttributeDAG;
+  pairs: CrossoverPair[];
+  onTogglePair: (pairId: string) => void;
+  isDark: boolean;
+}
+
+interface MatrixCell {
+  nodeA: DAGNode;
+  nodeB: DAGNode;
+  pair: CrossoverPair | null;
+}
+
+export function CrossoverMatrix({
+  dagA,
+  dagB,
+  pairs,
+  onTogglePair,
+  isDark,
+}: CrossoverMatrixProps) {
+  // Group nodes by category
+  const groupByCategory = (nodes: DAGNode[]) => {
+    return nodes.reduce((acc, node) => {
+      if (!acc[node.category]) acc[node.category] = [];
+      acc[node.category].push(node);
+      return acc;
+    }, {} as Record<string, DAGNode[]>);
+  };
+
+  const nodesByCategoryA = useMemo(() => groupByCategory(dagA.nodes), [dagA.nodes]);
+  const nodesByCategoryB = useMemo(() => groupByCategory(dagB.nodes), [dagB.nodes]);
+
+  // Create a lookup map for pairs
+  const pairLookup = useMemo(() => {
+    const lookup = new Map<string, CrossoverPair>();
+    for (const pair of pairs) {
+      // Key format: sourceNodeId-targetNodeId
+      lookup.set(`${pair.sourceNode.id}-${pair.targetNode.id}`, pair);
+    }
+    return lookup;
+  }, [pairs]);
+
+  // Get all categories from both DAGs
+  const categoriesA = Object.keys(nodesByCategoryA);
+  const categoriesB = Object.keys(nodesByCategoryB);
+
+  // Render a matrix for each category pair
+  const renderCategoryMatrix = (categoryA: string, categoryB: string) => {
+    const nodesA = nodesByCategoryA[categoryA] || [];
+    const nodesB = nodesByCategoryB[categoryB] || [];
+
+    if (nodesA.length === 0 || nodesB.length === 0) return null;
+
+    // Create matrix data
+    const matrixData = nodesA.map((nodeA) => {
+      const row: Record<string, MatrixCell | DAGNode> = { nodeA };
+      for (const nodeB of nodesB) {
+        const pair = pairLookup.get(`${nodeA.id}-${nodeB.id}`) ||
+                     pairLookup.get(`${nodeB.id}-${nodeA.id}`) ||
+                     null;
+        row[nodeB.id] = { nodeA, nodeB, pair };
+      }
+      return row;
+    });
+
+    // Create columns
+    const columns = [
+      {
+        title: (
+          <div style={{ display: 'flex', alignItems: 'center', gap: 4 }}>
+            <Tag color="blue">A</Tag>
+            <Text strong>{categoryA}</Text>
+          </div>
+        ),
+        dataIndex: 'nodeA',
+        key: 'nodeA',
+        width: 120,
+        fixed: 'left' as const,
+        render: (node: DAGNode) => (
+          <Text ellipsis style={{ maxWidth: 100 }}>{node.name}</Text>
+        ),
+      },
+      ...nodesB.map((nodeB) => ({
+        title: (
+          <Tooltip title={nodeB.name}>
+            <Text ellipsis style={{ maxWidth: 80 }}>{nodeB.name}</Text>
+          </Tooltip>
+        ),
+        dataIndex: nodeB.id,
+        key: nodeB.id,
+        width: 60,
+        align: 'center' as const,
+        render: (cell: MatrixCell) => {
+          if (!cell || !cell.pair) {
+            return <div style={{ color: isDark ? '#555' : '#ddd' }}>-</div>;
+          }
+          return (
+            <Checkbox
+              checked={cell.pair.selected}
+              onChange={() => onTogglePair(cell.pair!.id)}
+            />
+          );
+        },
+      })),
+    ];
+
+    return (
+      <div key={`${categoryA}-${categoryB}`} style={{ marginBottom: 16 }}>
+        <div style={{
+          display: 'flex',
+          alignItems: 'center',
+          gap: 8,
+          marginBottom: 8,
+          padding: '4px 8px',
+          background: isDark ? '#1f1f1f' : '#fafafa',
+          borderRadius: 4,
+        }}>
+          <Tag color="blue">A: {categoryA}</Tag>
+          <Text type="secondary">×</Text>
+          <Tag color="green">B: {categoryB}</Tag>
+        </div>
+        <Table
+          dataSource={matrixData}
+          columns={columns}
+          pagination={false}
+          size="small"
+          scroll={{ x: 'max-content' }}
+          rowKey={(row) => (row.nodeA as DAGNode).id}
+          bordered
+        />
+      </div>
+    );
+  };
+
+  // Render matrices for matching categories (same category in both paths)
+  const matchingCategories = categoriesA.filter(cat => categoriesB.includes(cat));
+
+  return (
+    <div style={{ padding: 16 }}>
+      {matchingCategories.length > 0 ? (
+        <>
+          <Text type="secondary" style={{ display: 'block', marginBottom: 16 }}>
+            Select attribute pairs to cross between Path A and Path B
+          </Text>
+          {matchingCategories.map(category =>
+            renderCategoryMatrix(category, category)
+          )}
+        </>
+      ) : (
+        <Text type="secondary">
+          No matching categories found between the two paths
+        </Text>
+      )}
+    </div>
+  );
+}

frontend/src/components/crossover/CrossoverPreview.tsx

@@ -0,0 +1,189 @@
+import { useMemo } from 'react';
+import { Card, Tag, Typography, Space, Empty, Divider } from 'antd';
+import { SwapOutlined, ArrowRightOutlined } from '@ant-design/icons';
+import type { CrossoverPair, AttributeDAG } from '../../types';
+
+const { Text } = Typography;
+
+interface CrossoverPreviewProps {
+  selectedPairs: CrossoverPair[];
+  dagA: AttributeDAG | null;
+  dagB: AttributeDAG | null;
+  isDark: boolean;
+}
+
+interface GroupedAttribute {
+  category: string;
+  attributes: string[];
+}
+
+export function CrossoverPreview({
+  selectedPairs,
+  dagA,
+  dagB,
+  isDark,
+}: CrossoverPreviewProps) {
+  // Group selected attributes by path and category
+  const { pathAGroups, pathBGroups, crossPairs } = useMemo(() => {
+    const pathAMap = new Map<string, Set<string>>();
+    const pathBMap = new Map<string, Set<string>>();
+    const crosses: Array<{
+      sourceAttr: string;
+      sourceCategory: string;
+      sourcePath: string;
+      targetAttr: string;
+      targetCategory: string;
+      targetPath: string;
+    }> = [];
+
+    for (const pair of selectedPairs) {
+      // Track source attributes
+      const sourceKey = pair.sourceNode.category;
+      if (!pathAMap.has(sourceKey)) pathAMap.set(sourceKey, new Set());
+      pathAMap.get(sourceKey)!.add(pair.sourceNode.name);
+
+      // Track target attributes
+      const targetKey = pair.targetNode.category;
+      if (!pathBMap.has(targetKey)) pathBMap.set(targetKey, new Set());
+      pathBMap.get(targetKey)!.add(pair.targetNode.name);
+
+      // Track cross pairs for visualization
+      crosses.push({
+        sourceAttr: pair.sourceNode.name,
+        sourceCategory: pair.sourceNode.category,
+        sourcePath: pair.sourcePathId,
+        targetAttr: pair.targetNode.name,
+        targetCategory: pair.targetNode.category,
+        targetPath: pair.targetPathId,
+      });
+    }
+
+    const pathAGroups: GroupedAttribute[] = Array.from(pathAMap.entries()).map(
+      ([category, attrs]) => ({ category, attributes: Array.from(attrs) })
+    );
+    const pathBGroups: GroupedAttribute[] = Array.from(pathBMap.entries()).map(
+      ([category, attrs]) => ({ category, attributes: Array.from(attrs) })
+    );
+
+    return { pathAGroups, pathBGroups, crossPairs: crosses };
+  }, [selectedPairs]);
+
+  if (selectedPairs.length === 0) {
+    return (
+      <Card size="small" style={{ marginBottom: 16 }}>
+        <Empty
+          description="No pairs selected"
+          image={Empty.PRESENTED_IMAGE_SIMPLE}
+        />
+      </Card>
+    );
+  }
+
+  const cardStyle = {
+    background: isDark ? '#1f1f1f' : '#fafafa',
+    borderRadius: 8,
+  };
+
+  const renderPathSummary = (
+    label: string,
+    color: 'blue' | 'green',
+    groups: GroupedAttribute[],
+    query: string | undefined
+  ) => (
+    <div style={{ flex: 1, minWidth: 150 }}>
+      <div style={{ marginBottom: 8 }}>
+        <Tag color={color}>{label}</Tag>
+        {query && (
+          <Text strong style={{ marginLeft: 4 }}>
+            {query}
+          </Text>
+        )}
+      </div>
+      <Space direction="vertical" size={4} style={{ width: '100%' }}>
+        {groups.map(({ category, attributes }) => (
+          <div key={category}>
+            <Text type="secondary" style={{ fontSize: 11 }}>
+              {category}:
+            </Text>
+            <div style={{ marginLeft: 8 }}>
+              {attributes.map((attr, i) => (
+                <Tag
+                  key={i}
+                  style={{
+                    margin: '2px 4px 2px 0',
+                    fontSize: 11,
+                    padding: '0 6px',
+                  }}
+                >
+                  {attr}
+                </Tag>
+              ))}
+            </div>
+          </div>
+        ))}
+      </Space>
+    </div>
+  );
+
+  // Get unique cross pairs for display (limit to first 5)
+  const uniqueCrosses = crossPairs.slice(0, 5);
+
+  return (
+    <Card
+      size="small"
+      title={
+        <Space>
+          <SwapOutlined />
+          <Text strong>Selection Preview</Text>
+          <Tag>{selectedPairs.length} pairs</Tag>
+        </Space>
+      }
+      style={{ marginBottom: 16 }}
+    >
+      {/* Side by side path summary */}
+      <div style={{ display: 'flex', gap: 16, marginBottom: 16 }}>
+        {renderPathSummary('Path A', 'blue', pathAGroups, dagA?.query)}
+        <Divider type="vertical" style={{ height: 'auto' }} />
+        {renderPathSummary('Path B', 'green', pathBGroups, dagB?.query)}
+      </div>
+
+      {/* Cross visualization */}
+      <div style={cardStyle}>
+        <div style={{ padding: 12 }}>
+          <Text type="secondary" style={{ fontSize: 12, display: 'block', marginBottom: 8 }}>
+            Sample Crossovers:
+          </Text>
+          <Space direction="vertical" size={4} style={{ width: '100%' }}>
+            {uniqueCrosses.map((cross, i) => (
+              <div
+                key={i}
+                style={{
+                  display: 'flex',
+                  alignItems: 'center',
+                  gap: 8,
+                  padding: '4px 8px',
+                  background: isDark ? '#141414' : '#fff',
+                  borderRadius: 4,
+                  fontSize: 12,
+                }}
+              >
+                <Tag color="blue" style={{ margin: 0 }}>A</Tag>
+                <Text ellipsis style={{ maxWidth: 100 }}>{cross.sourceAttr}</Text>
+                <Text type="secondary" style={{ fontSize: 10 }}>({cross.sourceCategory})</Text>
+                <ArrowRightOutlined style={{ color: isDark ? '#666' : '#999' }} />
+                <Tag color="green" style={{ margin: 0 }}>B</Tag>
+                <Text ellipsis style={{ maxWidth: 100 }}>{cross.targetAttr}</Text>
+                <Text type="secondary" style={{ fontSize: 10 }}>({cross.targetCategory})</Text>
+              </div>
+            ))}
+            {crossPairs.length > 5 && (
+              <Text type="secondary" style={{ fontSize: 11 }}>
+                ... and {crossPairs.length - 5} more pairs
+              </Text>
+            )}
+          </Space>
+        </div>
+      </div>
+    </Card>
+  );
+}

frontend/src/components/transformation/nodes/OriginalAttributeNode.tsx

@@ -1,36 +1,73 @@
 import { memo } from 'react';
+import { SwapOutlined } from '@ant-design/icons';
 
 interface OriginalAttributeNodeProps {
   data: {
     label: string;
     color: string;
     isDark: boolean;
+    crossoverSource?: string; // The query from which this attribute was crossed over
   };
 }
 
 export const OriginalAttributeNode = memo(({ data }: OriginalAttributeNodeProps) => {
-  const { label, color, isDark } = data;
+  const { label, color, isDark, crossoverSource } = data;
+  const isCrossover = !!crossoverSource;
 
   return (
     <div
       style={{
-        padding: '8px 16px',
+        position: 'relative',
+        padding: isCrossover ? '8px 16px 8px 12px' : '8px 16px',
         borderRadius: 8,
         background: isDark
           ? `linear-gradient(135deg, ${color}22 0%, ${color}0a 100%)`
           : `linear-gradient(135deg, ${color}1a 0%, ${color}05 100%)`,
-        border: `1px solid ${color}33`,
+        border: isCrossover
+          ? `2px dashed ${isDark ? '#faad14' : '#d48806'}`
+          : `1px solid ${color}33`,
         color: isDark ? 'rgba(255,255,255,0.9)' : 'rgba(0,0,0,0.85)',
         fontSize: '13px',
         fontWeight: 500,
         textAlign: 'center',
         whiteSpace: 'nowrap',
         userSelect: 'none',
-        boxShadow: '0 2px 6px rgba(0,0,0,0.05)',
+        boxShadow: isCrossover
+          ? '0 2px 8px rgba(250, 173, 20, 0.3)'
+          : '0 2px 6px rgba(0,0,0,0.05)',
       }}
     >
+      {isCrossover && (
+        <SwapOutlined
+          style={{
+            position: 'absolute',
+            top: -8,
+            left: -8,
+            fontSize: 14,
+            color: isDark ? '#faad14' : '#d48806',
+            background: isDark ? '#1f1f1f' : '#fff',
+            borderRadius: '50%',
+            padding: 2,
+            border: `1px solid ${isDark ? '#faad14' : '#d48806'}`,
+          }}
+        />
+      )}
+      <div style={{ display: 'flex', alignItems: 'center', gap: 6 }}>
         {label}
       </div>
+      {isCrossover && (
+        <div
+          style={{
+            fontSize: 9,
+            color: isDark ? '#faad14' : '#d48806',
+            marginTop: 2,
+            fontWeight: 400,
+          }}
+        >
+          ← from {crossoverSource}
+        </div>
+      )}
+    </div>
   );
 });
 

frontend/src/components/transformation/useExpertTransformationLayout.ts

@@ -5,6 +5,7 @@ import type { ExpertTransformationDAGResult, CategoryDefinition } from '../../ty
 interface LayoutConfig {
   isDark: boolean;
   fontSize?: number;
+  crossoverSource?: string; // If set, marks all attributes as crossed over from this source
 }
 
 const COLOR_PALETTE = [
@@ -39,7 +40,7 @@ export function useExpertTransformationLayout(
       return { nodes: [], edges: [] };
     }
 
-    const { isDark, fontSize = 13 } = config;
+    const { isDark, fontSize = 13, crossoverSource } = config;
     const nodes: Node[] = [];
     const edges: Edge[] = [];
 
@@ -233,6 +234,7 @@ export function useExpertTransformationLayout(
             label: group.attribute,
             color,
             isDark,
+            crossoverSource, // Mark as crossover if source is provided
           },
           draggable: false,
           selectable: false,

frontend/src/hooks/useAttribute.ts

@@ -1,7 +1,8 @@
 import { useState, useCallback } from 'react';
 import type {
   AttributeDAG,
-  DAGStreamAnalyzeResponse
+  DAGStreamAnalyzeResponse,
+  PromptLanguage
 } from '../types';
 import { CategoryMode } from '../types';
 import { analyzeAttributesStream } from '../services/api';
@@ -34,12 +35,13 @@ export function useAttribute() {
     chainCount: number = 5,
     categoryMode: CategoryMode = CategoryMode.DYNAMIC_AUTO,
     customCategories?: string[],
-    suggestedCategoryCount: number = 3
+    suggestedCategoryCount: number = 3,
+    lang: PromptLanguage = 'zh'
   ) => {
-    // 重置狀態
+    // Reset state
     setProgress({
       step: 'idle',
-      message: '準備開始分析...',
+      message: lang === 'zh' ? '準備開始分析...' : 'Preparing analysis...',
     });
     setError(null);
     setCurrentResult(null);
@@ -53,62 +55,63 @@ export function useAttribute() {
           temperature,
           category_mode: categoryMode,
           custom_categories: customCategories,
-          suggested_category_count: suggestedCategoryCount
+          suggested_category_count: suggestedCategoryCount,
+          lang
         },
         {
           onStep0Start: () => {
             setProgress({
               step: 'step0',
-              message: '正在分析類別...',
+              message: lang === 'zh' ? '正在分析類別...' : 'Analyzing categories...',
             });
           },
 
           onStep0Complete: () => {
             setProgress(prev => ({
               ...prev,
-              message: '類別分析完成',
+              message: lang === 'zh' ? '類別分析完成' : 'Category analysis complete',
             }));
           },
 
           onCategoriesResolved: (categories) => {
             setProgress(prev => ({
               ...prev,
-              message: `使用 ${categories.length} 個類別`,
+              message: lang === 'zh' ? `使用 ${categories.length} 個類別` : `Using ${categories.length} categories`,
             }));
           },
 
           onStep1Start: () => {
             setProgress({
               step: 'step1',
-              message: '正在分析物件屬性列表...',
+              message: lang === 'zh' ? '正在分析物件屬性列表...' : 'Analyzing object attributes...',
             });
           },
 
           onStep1Complete: () => {
             setProgress(prev => ({
               ...prev,
-              message: '屬性列表分析完成',
+              message: lang === 'zh' ? '屬性列表分析完成' : 'Attribute analysis complete',
             }));
           },
 
           onRelationshipsStart: () => {
             setProgress({
               step: 'relationships',
-              message: '正在生成關係...',
+              message: lang === 'zh' ? '正在生成關係...' : 'Generating relationships...',
             });
           },
 
           onRelationshipsComplete: (count) => {
             setProgress(prev => ({
               ...prev,
-              message: `生成 ${count} 個關係`,
+              message: lang === 'zh' ? `生成 ${count} 個關係` : `Generated ${count} relationships`,
             }));
           },
 
           onDone: (response: DAGStreamAnalyzeResponse) => {
             setProgress({
               step: 'done',
-              message: '分析完成！',
+              message: lang === 'zh' ? '分析完成！' : 'Analysis complete!',
             });
             setCurrentResult(response.dag);
 
@@ -126,7 +129,7 @@ export function useAttribute() {
             setProgress({
               step: 'error',
               error: errorMsg,
-              message: `錯誤: ${errorMsg}`,
+              message: lang === 'zh' ? `錯誤: ${errorMsg}` : `Error: ${errorMsg}`,
             });
             setError(errorMsg);
           },
@@ -137,19 +140,19 @@ export function useAttribute() {
       setProgress({
         step: 'error',
         error: errorMessage,
-        message: `錯誤: ${errorMessage}`,
+        message: lang === 'zh' ? `錯誤: ${errorMessage}` : `Error: ${errorMessage}`,
       });
       setError(errorMessage);
       throw err;
     }
   }, []);
 
-  const loadFromHistory = useCallback((item: DAGHistoryItem) => {
+  const loadFromHistory = useCallback((item: DAGHistoryItem, lang: PromptLanguage = 'zh') => {
     setCurrentResult(item.result);
     setError(null);
     setProgress({
       step: 'done',
-      message: '從歷史記錄載入',
+      message: lang === 'zh' ? '從歷史記錄載入' : 'Loaded from history',
     });
   }, []);
 

frontend/src/hooks/useAttributeCrossover.ts

@@ -0,0 +1,182 @@
+import { useState, useCallback, useMemo } from 'react';
+import type {
+  AttributeDAG,
+  DAGNode,
+  CrossoverPair,
+  CrossoverConfig,
+  CrossoverResult,
+} from '../types';
+
+const DEFAULT_CONFIG: CrossoverConfig = {
+  enabledCrossTypes: ['same-category', 'cross-category'],
+  maxPairsPerType: 10,
+  autoGenerate: true,
+};
+
+export function useAttributeCrossover() {
+  const [pairs, setPairs] = useState<CrossoverPair[]>([]);
+  const [config, setConfig] = useState<CrossoverConfig>(DEFAULT_CONFIG);
+
+  // Group nodes by category
+  const groupNodesByCategory = useCallback((nodes: DAGNode[]): Record<string, DAGNode[]> => {
+    return nodes.reduce((acc, node) => {
+      if (!acc[node.category]) acc[node.category] = [];
+      acc[node.category].push(node);
+      return acc;
+    }, {} as Record<string, DAGNode[]>);
+  }, []);
+
+  // Generate crossover pairs from two DAGs
+  const generatePairs = useCallback((
+    dagA: AttributeDAG,
+    dagB: AttributeDAG
+  ): CrossoverPair[] => {
+    const newPairs: CrossoverPair[] = [];
+
+    const nodesByCategoryA = groupNodesByCategory(dagA.nodes);
+    const nodesByCategoryB = groupNodesByCategory(dagB.nodes);
+
+    // Get all unique categories from both DAGs
+    const categoriesA = Object.keys(nodesByCategoryA);
+    const categoriesB = Object.keys(nodesByCategoryB);
+
+    // Strategy 1: Cross matching categories (A's category X with B's category X)
+    // This helps find how different objects approach the same category differently
+    for (const category of categoriesA) {
+      if (nodesByCategoryB[category]) {
+        const nodesA = nodesByCategoryA[category].slice(0, config.maxPairsPerType);
+        const nodesB = nodesByCategoryB[category].slice(0, config.maxPairsPerType);
+
+        for (const nodeA of nodesA) {
+          for (const nodeB of nodesB) {
+            newPairs.push({
+              id: `same-${nodeA.id}-${nodeB.id}`,
+              sourcePathId: 'A',
+              sourceNode: nodeA,
+              targetPathId: 'B',
+              targetNode: nodeB,
+              crossType: `same-${category}`,
+              selected: true,  // Default selected for same-category pairs
+            });
+          }
+        }
+      }
+    }
+
+    // Strategy 2: Cross different categories between A and B
+    // This creates innovative combinations (e.g., A's materials with B's functions)
+    for (const categoryA of categoriesA) {
+      for (const categoryB of categoriesB) {
+        // Skip if same category (already handled above)
+        if (categoryA === categoryB) continue;
+
+        const nodesA = nodesByCategoryA[categoryA].slice(0, 3);
+        const nodesB = nodesByCategoryB[categoryB].slice(0, 3);
+
+        if (nodesA.length > 0 && nodesB.length > 0) {
+          for (const nodeA of nodesA) {
+            for (const nodeB of nodesB) {
+              newPairs.push({
+                id: `cross-${nodeA.id}-${nodeB.id}`,
+                sourcePathId: 'A',
+                sourceNode: nodeA,
+                targetPathId: 'B',
+                targetNode: nodeB,
+                crossType: `cross-${categoryA}-${categoryB}`,
+                selected: false,  // Cross-category pairs default to not selected
+              });
+            }
+          }
+        }
+      }
+    }
+
+    return newPairs;
+  }, [groupNodesByCategory, config.maxPairsPerType]);
+
+  // Apply generated pairs to state
+  const applyPairs = useCallback((dagA: AttributeDAG, dagB: AttributeDAG) => {
+    const newPairs = generatePairs(dagA, dagB);
+    setPairs(newPairs);
+    return newPairs;
+  }, [generatePairs]);
+
+  // Toggle pair selection
+  const togglePairSelection = useCallback((pairId: string) => {
+    setPairs(prev => prev.map(pair =>
+      pair.id === pairId
+        ? { ...pair, selected: !pair.selected }
+        : pair
+    ));
+  }, []);
+
+  // Select all pairs of a specific type
+  const selectPairsByType = useCallback((crossType: string, selected: boolean) => {
+    setPairs(prev => prev.map(pair =>
+      pair.crossType === crossType
+        ? { ...pair, selected }
+        : pair
+    ));
+  }, []);
+
+  // Select all pairs
+  const selectAll = useCallback((selected: boolean) => {
+    setPairs(prev => prev.map(pair => ({ ...pair, selected })));
+  }, []);
+
+  // Clear all pairs
+  const clearPairs = useCallback(() => {
+    setPairs([]);
+  }, []);
+
+  // Get pairs grouped by cross type
+  const pairsByType = useMemo(() => {
+    return pairs.reduce((acc, pair) => {
+      if (!acc[pair.crossType]) acc[pair.crossType] = [];
+      acc[pair.crossType].push(pair);
+      return acc;
+    }, {} as Record<string, CrossoverPair[]>);
+  }, [pairs]);
+
+  // Get selected pairs only
+  const selectedPairs = useMemo(() => {
+    return pairs.filter(p => p.selected);
+  }, [pairs]);
+
+  // Get cross type statistics
+  const crossTypeStats = useMemo(() => {
+    const stats: Record<string, { total: number; selected: number }> = {};
+    for (const pair of pairs) {
+      if (!stats[pair.crossType]) {
+        stats[pair.crossType] = { total: 0, selected: 0 };
+      }
+      stats[pair.crossType].total++;
+      if (pair.selected) {
+        stats[pair.crossType].selected++;
+      }
+    }
+    return stats;
+  }, [pairs]);
+
+  // Get result summary
+  const result: CrossoverResult = useMemo(() => ({
+    pairs,
+    totalPairs: pairs.length,
+  }), [pairs]);
+
+  return {
+    pairs,
+    selectedPairs,
+    pairsByType,
+    crossTypeStats,
+    config,
+    result,
+    setConfig,
+    generatePairs,
+    applyPairs,
+    togglePairSelection,
+    selectPairsByType,
+    selectAll,
+    clearPairs,
+  };
+}

frontend/src/hooks/useDeduplication.ts

@@ -5,6 +5,7 @@ import type {
   DeduplicationResult,
   DeduplicationProgress,
   DeduplicationMethod,
+  PromptLanguage,
 } from '../types';
 
 /**
@@ -25,14 +26,16 @@ export function useDeduplication() {
    * @param descriptions - List of descriptions to deduplicate
    * @param threshold - Similarity threshold (only used for embedding method)
    * @param method - Deduplication method: 'embedding' (fast) or 'llm' (accurate but slow)
+   * @param lang - Prompt language for LLM method
    */
   const deduplicate = useCallback(async (
     descriptions: ExpertTransformationDescription[],
     threshold: number = 0.85,
-    method: DeduplicationMethod = 'embedding'
+    method: DeduplicationMethod = 'embedding',
+    lang: PromptLanguage = 'zh'
   ) => {
     if (!descriptions || descriptions.length === 0) {
-      setError('No descriptions to deduplicate');
+      setError(lang === 'zh' ? '沒有可去重的描述' : 'No descriptions to deduplicate');
       return;
     }
 
@@ -40,12 +43,15 @@ export function useDeduplication() {
     setError(null);
     setResult(null);
 
-    // 根據方法顯示不同的進度訊息
     const methodLabel = method === 'embedding' ? 'Embedding' : 'LLM';
     const pairCount = (descriptions.length * (descriptions.length - 1)) / 2;
     const progressMessage = method === 'llm'
-      ? `Processing ${descriptions.length} descriptions with LLM (${pairCount} comparisons)...`
-      : `Processing ${descriptions.length} descriptions with ${methodLabel}...`;
+      ? (lang === 'zh'
+        ? `正在使用 LLM 處理 ${descriptions.length} 個描述（${pairCount} 次比較）...`
+        : `Processing ${descriptions.length} descriptions with LLM (${pairCount} comparisons)...`)
+      : (lang === 'zh'
+        ? `正在使用 ${methodLabel} 處理 ${descriptions.length} 個描述...`
+        : `Processing ${descriptions.length} descriptions with ${methodLabel}...`);
 
     setProgress({
       step: 'processing',
@@ -57,19 +63,22 @@ export function useDeduplication() {
         descriptions,
         similarity_threshold: threshold,
         method,
+        lang,
       });
 
       setResult(deduplicationResult);
       setProgress({
         step: 'done',
-        message: `Found ${deduplicationResult.total_groups} unique groups, ${deduplicationResult.total_duplicates} duplicates (${methodLabel})`,
+        message: lang === 'zh'
+          ? `發現 ${deduplicationResult.total_groups} 個獨特群組，${deduplicationResult.total_duplicates} 個重複（${methodLabel}）`
+          : `Found ${deduplicationResult.total_groups} unique groups, ${deduplicationResult.total_duplicates} duplicates (${methodLabel})`,
       });
     } catch (err) {
       const errorMessage = err instanceof Error ? err.message : 'Unknown error';
       setError(errorMessage);
       setProgress({
         step: 'error',
-        message: 'Deduplication failed',
+        message: lang === 'zh' ? '去重失敗' : 'Deduplication failed',
         error: errorMessage,
       });
     } finally {

frontend/src/hooks/useDualPathAttribute.ts

@@ -0,0 +1,218 @@
+import { useState, useCallback } from 'react';
+import type {
+  AttributeDAG,
+  DAGStreamAnalyzeResponse,
+  PathId,
+  PathState,
+  DualPathState,
+  DAGProgress,
+  CategoryMode,
+  PromptLanguage,
+} from '../types';
+import { CategoryMode as CategoryModeValues } from '../types';
+import { analyzeAttributesStream } from '../services/api';
+
+const initialProgress: DAGProgress = {
+  step: 'idle',
+  message: '',
+};
+
+const initialPathState: PathState = {
+  query: '',
+  result: null,
+  loading: false,
+  progress: initialProgress,
+  error: null,
+};
+
+export interface AnalyzeOptions {
+  model?: string;
+  temperature?: number;
+  chainCount?: number;
+  categoryMode?: CategoryMode;
+  customCategories?: string[];
+  suggestedCategoryCount?: number;
+  lang?: PromptLanguage;
+}
+
+export function useDualPathAttribute() {
+  const [state, setState] = useState<DualPathState>({
+    pathA: { ...initialPathState },
+    pathB: { ...initialPathState },
+  });
+
+  const getPathKey = (pathId: PathId): 'pathA' | 'pathB' =>
+    pathId === 'A' ? 'pathA' : 'pathB';
+
+  const updatePathState = useCallback((
+    pathId: PathId,
+    updates: Partial<PathState>
+  ) => {
+    const pathKey = getPathKey(pathId);
+    setState(prev => ({
+      ...prev,
+      [pathKey]: {
+        ...prev[pathKey],
+        ...updates,
+      },
+    }));
+  }, []);
+
+  const updatePathProgress = useCallback((
+    pathId: PathId,
+    step: DAGProgress['step'],
+    message: string,
+    error?: string
+  ) => {
+    updatePathState(pathId, {
+      progress: { step, message, error },
+    });
+  }, [updatePathState]);
+
+  const analyzePath = useCallback(async (
+    pathId: PathId,
+    query: string,
+    options: AnalyzeOptions = {}
+  ) => {
+    const {
+      model,
+      temperature,
+      chainCount = 5,
+      categoryMode = CategoryModeValues.DYNAMIC_AUTO,
+      customCategories,
+      suggestedCategoryCount = 3,
+      lang = 'zh',
+    } = options;
+
+    // Reset state for this path
+    updatePathState(pathId, {
+      query,
+      result: null,
+      loading: true,
+      error: null,
+      progress: { step: 'idle', message: lang === 'zh' ? '準備分析中...' : 'Preparing analysis...' },
+    });
+
+    try {
+      await analyzeAttributesStream(
+        {
+          query,
+          chain_count: chainCount,
+          model,
+          temperature,
+          category_mode: categoryMode,
+          custom_categories: customCategories,
+          suggested_category_count: suggestedCategoryCount,
+          lang,
+        },
+        {
+          onStep0Start: () => {
+            updatePathProgress(pathId, 'step0', lang === 'zh' ? '正在分析類別...' : 'Analyzing categories...');
+          },
+
+          onStep0Complete: () => {
+            updatePathProgress(pathId, 'step0', lang === 'zh' ? '類別分析完成' : 'Categories analyzed');
+          },
+
+          onCategoriesResolved: (categories) => {
+            updatePathProgress(pathId, 'step0', lang === 'zh' ? `使用 ${categories.length} 個類別` : `Using ${categories.length} categories`);
+          },
+
+          onStep1Start: () => {
+            updatePathProgress(pathId, 'step1', lang === 'zh' ? '正在分析屬性...' : 'Analyzing attributes...');
+          },
+
+          onStep1Complete: () => {
+            updatePathProgress(pathId, 'step1', lang === 'zh' ? '屬性分析完成' : 'Attributes analyzed');
+          },
+
+          onRelationshipsStart: () => {
+            updatePathProgress(pathId, 'relationships', lang === 'zh' ? '正在生成關係...' : 'Generating relationships...');
+          },
+
+          onRelationshipsComplete: (count) => {
+            updatePathProgress(pathId, 'relationships', lang === 'zh' ? `生成 ${count} 個關係` : `Generated ${count} relationships`);
+          },
+
+          onDone: (response: DAGStreamAnalyzeResponse) => {
+            updatePathState(pathId, {
+              result: response.dag,
+              loading: false,
+              progress: { step: 'done', message: lang === 'zh' ? '分析完成！' : 'Analysis complete!' },
+            });
+          },
+
+          onError: (errorMsg) => {
+            updatePathState(pathId, {
+              loading: false,
+              error: errorMsg,
+              progress: { step: 'error', message: lang === 'zh' ? `錯誤: ${errorMsg}` : `Error: ${errorMsg}`, error: errorMsg },
+            });
+          },
+        }
+      );
+    } catch (err) {
+      const errorMessage = err instanceof Error ? err.message : 'Unknown error';
+      updatePathState(pathId, {
+        loading: false,
+        error: errorMessage,
+        progress: { step: 'error', message: lang === 'zh' ? `錯誤: ${errorMessage}` : `Error: ${errorMessage}`, error: errorMessage },
+      });
+    }
+  }, [updatePathState, updatePathProgress]);
+
+  const analyzeParallel = useCallback(async (
+    queryA: string,
+    queryB: string,
+    options: AnalyzeOptions = {}
+  ) => {
+    // Run both analyses in parallel
+    await Promise.all([
+      analyzePath('A', queryA, options),
+      analyzePath('B', queryB, options),
+    ]);
+  }, [analyzePath]);
+
+  const clearPath = useCallback((pathId: PathId) => {
+    updatePathState(pathId, { ...initialPathState });
+  }, [updatePathState]);
+
+  const clearAll = useCallback(() => {
+    setState({
+      pathA: { ...initialPathState },
+      pathB: { ...initialPathState },
+    });
+  }, []);
+
+  const setPathResult = useCallback((pathId: PathId, result: AttributeDAG) => {
+    updatePathState(pathId, {
+      result,
+      loading: false,
+      progress: { step: 'done', message: 'Loaded from history' },
+    });
+  }, [updatePathState]);
+
+  // Computed properties
+  const pathA = state.pathA;
+  const pathB = state.pathB;
+  const bothLoading = pathA.loading && pathB.loading;
+  const anyLoading = pathA.loading || pathB.loading;
+  const bothComplete = pathA.result !== null && pathB.result !== null;
+  const bothIdle = pathA.progress.step === 'idle' && pathB.progress.step === 'idle';
+
+  return {
+    state,
+    pathA,
+    pathB,
+    analyzePath,
+    analyzeParallel,
+    clearPath,
+    clearAll,
+    setPathResult,
+    // Status flags
+    bothLoading,
+    anyLoading,
+    bothComplete,
+    bothIdle,
+  };
+}

frontend/src/hooks/useExpertTransformation.ts

@@ -8,6 +8,7 @@ import type {
   ExpertProfile,
   CategoryDefinition,
   ExpertSource,
+  PromptLanguage,
 } from '../types';
 
 interface UseExpertTransformationOptions {
@@ -15,6 +16,7 @@ interface UseExpertTransformationOptions {
   temperature?: number;
   expertSource?: ExpertSource;
   expertLanguage?: 'en' | 'zh';
+  lang?: PromptLanguage;
 }
 
 export function useExpertTransformation(options: UseExpertTransformationOptions = {}) {
@@ -48,11 +50,12 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
       return new Promise((resolve) => {
         let categoryExperts: ExpertProfile[] = [];
 
+        const lang = options.lang || 'zh';
         setProgress((prev) => ({
           ...prev,
           step: 'expert',
           currentCategory: category.name,
-          message: `組建專家團隊...`,
+          message: lang === 'zh' ? '組建專家團隊...' : 'Building expert team...',
         }));
 
         expertTransformCategoryStream(
@@ -67,13 +70,14 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
             expert_language: options.expertLanguage,
             model: options.model,
             temperature: options.temperature,
+            lang,
           },
           {
             onExpertStart: () => {
               setProgress((prev) => ({
                 ...prev,
                 step: 'expert',
-                message: `正在組建專家團隊...`,
+                message: lang === 'zh' ? '正在組建專家團隊...' : 'Building expert team...',
               }));
             },
             onExpertComplete: (expertsData) => {
@@ -82,40 +86,40 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
               setProgress((prev) => ({
                 ...prev,
                 experts: expertsData,
-                message: `專家團隊組建完成（${expertsData.length}位專家）`,
+                message: lang === 'zh' ? `專家團隊組建完成（${expertsData.length}位專家）` : `Expert team ready (${expertsData.length} experts)`,
               }));
             },
             onKeywordStart: () => {
               setProgress((prev) => ({
                 ...prev,
                 step: 'keyword',
-                message: `專家團隊為「${category.name}」的屬性生成關鍵字...`,
+                message: lang === 'zh' ? `專家團隊為「${category.name}」的屬性生成關鍵字...` : `Experts generating keywords for "${category.name}"...`,
               }));
             },
             onKeywordProgress: (data) => {
               setProgress((prev) => ({
                 ...prev,
                 currentAttribute: data.attribute,
-                message: `為「${data.attribute}」生成了 ${data.count} 個關鍵字`,
+                message: lang === 'zh' ? `為「${data.attribute}」生成了 ${data.count} 個關鍵字` : `Generated ${data.count} keywords for "${data.attribute}"`,
               }));
             },
             onKeywordComplete: (totalKeywords) => {
               setProgress((prev) => ({
                 ...prev,
-                message: `共生成了 ${totalKeywords} 個專家關鍵字`,
+                message: lang === 'zh' ? `共生成了 ${totalKeywords} 個專家關鍵字` : `Generated ${totalKeywords} expert keywords`,
               }));
             },
             onDescriptionStart: () => {
               setProgress((prev) => ({
                 ...prev,
                 step: 'description',
-                message: `為「${category.name}」的專家關鍵字生成創新描述...`,
+                message: lang === 'zh' ? `為「${category.name}」的專家關鍵字生成創新描述...` : `Generating descriptions for "${category.name}" keywords...`,
               }));
             },
             onDescriptionComplete: (count) => {
               setProgress((prev) => ({
                 ...prev,
-                message: `生成了 ${count} 個創新描述`,
+                message: lang === 'zh' ? `生成了 ${count} 個創新描述` : `Generated ${count} descriptions`,
               }));
             },
             onDone: (data) => {
@@ -123,7 +127,7 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
                 ...prev,
                 step: 'done',
                 processedCategories: [...prev.processedCategories, category.name],
-                message: `「${category.name}」處理完成`,
+                message: lang === 'zh' ? `「${category.name}」處理完成` : `"${category.name}" complete`,
               }));
               resolve({
                 result: data.result,
@@ -135,7 +139,7 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
                 ...prev,
                 step: 'error',
                 error: err,
-                message: `處理「${category.name}」時發生錯誤`,
+                message: lang === 'zh' ? `處理「${category.name}」時發生錯誤` : `Error processing "${category.name}"`,
               }));
               resolve({
                 result: null,
@@ -148,7 +152,7 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
             ...prev,
             step: 'error',
             error: err.message,
-            message: `處理「${category.name}」時發生錯誤`,
+            message: lang === 'zh' ? `處理「${category.name}」時發生錯誤` : `Error processing "${category.name}"`,
           }));
           resolve({
             result: null,
@@ -157,11 +161,12 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
         });
       });
     },
-    [options.model, options.temperature, options.expertSource, options.expertLanguage]
+    [options.model, options.temperature, options.expertSource, options.expertLanguage, options.lang]
   );
 
   const transformAll = useCallback(
     async (input: ExpertTransformationInput) => {
+      const lang = options.lang || 'zh';
       setLoading(true);
       setError(null);
       setResults(null);
@@ -170,7 +175,7 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
         step: 'idle',
         currentCategory: '',
         processedCategories: [],
-        message: '開始處理...',
+        message: lang === 'zh' ? '開始處理...' : 'Starting...',
       });
 
       const categoryResults: ExpertTransformationCategoryResult[] = [];
@@ -210,12 +215,12 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
       setProgress((prev) => ({
         ...prev,
         step: 'done',
-        message: '所有類別處理完成',
+        message: lang === 'zh' ? '所有類別處理完成' : 'All categories complete',
       }));
 
       return finalResult;
     },
-    [transformCategory]
+    [transformCategory, options.lang]
   );
 
   const clearResults = useCallback(() => {

frontend/src/services/api.ts

@@ -12,11 +12,15 @@ import type {
   ExpertTransformationCategoryResult,
   ExpertProfile,
   DeduplicationRequest,
-  DeduplicationResult
+  DeduplicationResult,
+  PatentSearchRequest,
+  PatentSearchResponse,
+  BatchPatentSearchRequest,
+  BatchPatentSearchResponse,
 } from '../types';
 
 // 自動使用當前瀏覽器的 hostname，支援遠端存取
-const API_BASE_URL = `http://${window.location.hostname}:8000/api`;
+const API_BASE_URL = `http://${window.location.hostname}:8001/api`;
 
 export interface SSECallbacks {
   onStep0Start?: () => void;
@@ -322,3 +326,43 @@ export async function deduplicateDescriptions(
 
   return response.json();
 }
+
+// ===== Patent Search API =====
+
+export async function searchPatents(
+  request: PatentSearchRequest
+): Promise<PatentSearchResponse> {
+  const response = await fetch(`${API_BASE_URL}/patent/search`, {
+    method: 'POST',
+    headers: {
+      'Content-Type': 'application/json',
+    },
+    body: JSON.stringify(request),
+  });
+
+  if (!response.ok) {
+    const errorText = await response.text();
+    throw new Error(`API error: ${response.status} - ${errorText}`);
+  }
+
+  return response.json();
+}
+
+export async function batchSearchPatents(
+  request: BatchPatentSearchRequest
+): Promise<BatchPatentSearchResponse> {
+  const response = await fetch(`${API_BASE_URL}/patent/search/batch`, {
+    method: 'POST',
+    headers: {
+      'Content-Type': 'application/json',
+    },
+    body: JSON.stringify(request),
+  });
+
+  if (!response.ok) {
+    const errorText = await response.text();
+    throw new Error(`API error: ${response.status} - ${errorText}`);
+  }
+
+  return response.json();
+}

frontend/src/types/index.ts

@@ -1,6 +1,9 @@
+// ===== Language type =====
+export type PromptLanguage = 'zh' | 'en';
+
 export interface AttributeNode {
   name: string;
-  category?: string;  // 材料, 功能, 用途, 使用族群
+  category?: string;  // Materials, Functions, Usages, User Groups
   children?: AttributeNode[];
 }
 
@@ -9,16 +12,29 @@ export interface AnalyzeRequest {
   model?: string;
   temperature?: number;
   categories?: string[];
+  lang?: PromptLanguage;
 }
 
-export const DEFAULT_CATEGORIES = ['材料', '功能', '用途', '使用族群', '特性'];
+export const DEFAULT_CATEGORIES = {
+  zh: ['材料', '功能', '用途', '使用族群', '特性'],
+  en: ['Materials', 'Functions', 'Usages', 'User Groups', 'Characteristics'],
+};
 
-export const CATEGORY_DESCRIPTIONS: Record<string, string> = {
+export const CATEGORY_DESCRIPTIONS: Record<PromptLanguage, Record<string, string>> = {
+  zh: {
     '材料': '物件由什麼材料組成',
     '功能': '物件能做什麼',
     '用途': '物件在什麼場景使用',
     '使用族群': '誰會使用這個物件',
     '特性': '物件有什麼特徵',
+  },
+  en: {
+    'Materials': 'What materials the object is made of',
+    'Functions': 'What the object can do',
+    'Usages': 'In what scenarios the object is used',
+    'User Groups': 'Who uses this object',
+    'Characteristics': 'What features the object has',
+  },
 };
 
 export interface AnalyzeResponse {
@@ -92,6 +108,8 @@ export interface StreamAnalyzeRequest {
   category_mode?: CategoryMode;
   custom_categories?: string[];
   suggested_category_count?: number;
+  // Language setting
+  lang?: PromptLanguage;
 }
 
 export interface StreamProgress {
@@ -161,6 +179,7 @@ export interface TransformationRequest {
   model?: string;
   temperature?: number;
   keyword_count?: number;
+  lang?: PromptLanguage;
 }
 
 export interface TransformationDescription {
@@ -238,11 +257,12 @@ export interface ExpertTransformationRequest {
   attributes: string[];
   expert_count: number;              // 2-8
   keywords_per_expert: number;       // 1-3
-  custom_experts?: string[];         // ["藥師", "工程師"]
-  expert_source?: ExpertSource;      // 專家來源 (default: 'llm')
-  expert_language?: string;          // 外部來源語言 (default: 'en')
+  custom_experts?: string[];         // User-specified experts
+  expert_source?: ExpertSource;      // Expert source (default: 'llm')
+  expert_language?: string;          // External source language (default: 'en')
   model?: string;
   temperature?: number;
+  lang?: PromptLanguage;             // Prompt language
 }
 
 export interface ExpertTransformationProgress {
@@ -272,9 +292,10 @@ export type DeduplicationMethod = 'embedding' | 'llm';
 
 export interface DeduplicationRequest {
   descriptions: ExpertTransformationDescription[];
-  method?: DeduplicationMethod;  // 去重方法，default: 'embedding'
-  similarity_threshold?: number;  // 0.0-1.0, default 0.85，僅 embedding 使用
+  method?: DeduplicationMethod;  // Deduplication method, default: 'embedding'
+  similarity_threshold?: number;  // 0.0-1.0, default 0.85, only for embedding
   model?: string;  // Embedding/LLM model
+  lang?: PromptLanguage;  // Prompt language (for LLM method)
 }
 
 export interface DescriptionGroup {
@@ -299,3 +320,127 @@ export interface DeduplicationProgress {
   message: string;
   error?: string;
 }
+
+// ===== Dual-Path types =====
+
+export type PathId = 'A' | 'B';
+
+export interface PathState {
+  query: string;
+  result: AttributeDAG | null;
+  loading: boolean;
+  progress: DAGProgress;
+  error: string | null;
+}
+
+export interface DualPathState {
+  pathA: PathState;
+  pathB: PathState;
+}
+
+export interface DAGProgress {
+  step: 'idle' | 'step0' | 'step1' | 'relationships' | 'done' | 'error';
+  message: string;
+  error?: string;
+}
+
+// ===== Attribute Crossover types =====
+
+export interface CrossoverPair {
+  id: string;
+  sourcePathId: PathId;
+  sourceNode: DAGNode;
+  targetPathId: PathId;
+  targetNode: DAGNode;
+  crossType: string;  // 'material-function' | 'function-usage' | etc.
+  selected: boolean;
+}
+
+export interface CrossoverConfig {
+  enabledCrossTypes: string[];
+  maxPairsPerType: number;
+  autoGenerate: boolean;
+}
+
+export interface CrossoverResult {
+  pairs: CrossoverPair[];
+  totalPairs: number;
+}
+
+// ===== Expert Mode types =====
+
+export type ExpertMode = 'shared' | 'independent';
+
+export interface ExpertConfig {
+  expert_count: number;
+  keywords_per_expert: number;
+  custom_experts?: string[];
+  expert_source: ExpertSource;
+  expert_language?: 'en' | 'zh';
+}
+
+export interface DualPathExpertConfig {
+  mode: ExpertMode;
+  sharedConfig?: ExpertConfig;
+  independentConfig?: {
+    pathA: ExpertConfig;
+    pathB: ExpertConfig;
+  };
+}
+
+// ===== Dual-Path Transformation types =====
+
+export interface DualPathTransformationState {
+  pathA: ExpertTransformationDAGResult | null;
+  pathB: ExpertTransformationDAGResult | null;
+  crossover: CrossoverTransformationResult | null;
+}
+
+export interface CrossoverTransformationResult {
+  crossoverPairs: CrossoverPair[];
+  experts: ExpertProfile[];
+  transformedIdeas: ExpertTransformationDescription[];
+}
+
+// ===== Patent Search types =====
+
+export interface PatentResult {
+  publication_number: string;
+  title: string;
+  snippet: string;
+  publication_date: string | null;
+  assignee: string | null;
+  inventor: string | null;
+  status: 'ACTIVE' | 'NOT_ACTIVE' | 'UNKNOWN';
+  pdf_url: string | null;
+  thumbnail_url: string | null;
+}
+
+export interface PatentSearchRequest {
+  query: string;
+  max_results?: number;
+}
+
+export interface PatentSearchResponse {
+  query: string;
+  total_results: number;
+  patents: PatentResult[];
+  error?: string | null;
+}
+
+export interface BatchPatentSearchRequest {
+  queries: string[];
+  max_results_per_query?: number;
+}
+
+export interface BatchPatentSearchResult {
+  query: string;
+  total_results: number;
+  patents: PatentResult[];
+  error?: string | null;
+}
+
+export interface BatchPatentSearchResponse {
+  results: BatchPatentSearchResult[];
+  total_queries: number;
+}

frontend/src/utils/crossoverToDAG.ts

@@ -0,0 +1,197 @@
+import type { CrossoverPair, AttributeDAG, CategoryDefinition, DAGNode } from '../types';
+
+/**
+ * Result of crossover transformation - two separate DAGs
+ * Each path receives attributes from the other path
+ */
+export interface CrossoverDAGResult {
+  // Path A receives attributes from Path B
+  pathA: AttributeDAG;
+  // Path B receives attributes from Path A
+  pathB: AttributeDAG;
+}
+
+/**
+ * Convert selected crossover pairs into two separate AttributeDAGs.
+ *
+ * The crossover logic:
+ * - When pair (A's "防水布" × B's "鋁合金") is selected:
+ *   - Path A gets "鋁合金" (from B) to transform with A's context
+ *   - Path B gets "防水布" (from A) to transform with B's context
+ *
+ * This allows experts to think about:
+ * - "What if umbrella had aluminum alloy?" (A gets B's attribute)
+ * - "What if bicycle had waterproof fabric?" (B gets A's attribute)
+ */
+export function crossoverPairsToDAGs(
+  pairs: CrossoverPair[],
+  dagA: AttributeDAG,
+  dagB: AttributeDAG
+): CrossoverDAGResult {
+  // Collect attributes that each path receives from the other
+  const attributesForA: Map<string, Set<string>> = new Map(); // category -> attributes from B
+  const attributesForB: Map<string, Set<string>> = new Map(); // category -> attributes from A
+
+  for (const pair of pairs) {
+    // Path A receives the target node (from B)
+    // Path B receives the source node (from A)
+    if (pair.sourcePathId === 'A' && pair.targetPathId === 'B') {
+      // A's attribute crossed with B's attribute
+      // A gets B's attribute, B gets A's attribute
+      const categoryForA = pair.targetNode.category;
+      const categoryForB = pair.sourceNode.category;
+
+      if (!attributesForA.has(categoryForA)) attributesForA.set(categoryForA, new Set());
+      if (!attributesForB.has(categoryForB)) attributesForB.set(categoryForB, new Set());
+
+      attributesForA.get(categoryForA)!.add(pair.targetNode.name);
+      attributesForB.get(categoryForB)!.add(pair.sourceNode.name);
+    } else if (pair.sourcePathId === 'B' && pair.targetPathId === 'A') {
+      // B's attribute crossed with A's attribute
+      // A gets B's attribute, B gets A's attribute (reverse direction)
+      const categoryForA = pair.sourceNode.category;
+      const categoryForB = pair.targetNode.category;
+
+      if (!attributesForA.has(categoryForA)) attributesForA.set(categoryForA, new Set());
+      if (!attributesForB.has(categoryForB)) attributesForB.set(categoryForB, new Set());
+
+      attributesForA.get(categoryForA)!.add(pair.sourceNode.name);
+      attributesForB.get(categoryForB)!.add(pair.targetNode.name);
+    }
+  }
+
+  // Build DAG for Path A (receives attributes from B)
+  const pathAResult = buildCrossoverDAG(
+    dagA.query,
+    attributesForA,
+    `Crossover: ${dagB.query} → ${dagA.query}`
+  );
+
+  // Build DAG for Path B (receives attributes from A)
+  const pathBResult = buildCrossoverDAG(
+    dagB.query,
+    attributesForB,
+    `Crossover: ${dagA.query} → ${dagB.query}`
+  );
+
+  return {
+    pathA: pathAResult,
+    pathB: pathBResult,
+  };
+}
+
+/**
+ * Build an AttributeDAG from crossover attributes
+ */
+function buildCrossoverDAG(
+  originalQuery: string,
+  attributesByCategory: Map<string, Set<string>>,
+  _crossoverDescription: string
+): AttributeDAG {
+  const categories: CategoryDefinition[] = [];
+  const nodes: DAGNode[] = [];
+  let nodeIndex = 0;
+  let categoryIndex = 0;
+
+  for (const [categoryName, attributes] of attributesByCategory.entries()) {
+    categories.push({
+      name: categoryName,
+      description: `Crossover attributes for ${categoryName}`,
+      is_fixed: false,
+      order: categoryIndex,
+    });
+    categoryIndex++;
+
+    for (const attrName of attributes) {
+      nodes.push({
+        id: `crossover-${nodeIndex}`,
+        name: attrName,
+        category: categoryName,
+        order: nodeIndex,
+      });
+      nodeIndex++;
+    }
+  }
+
+  return {
+    query: originalQuery,
+    categories,
+    nodes,
+    edges: [],
+  };
+}
+
+/**
+ * Legacy function - converts to single DAG (deprecated)
+ * Kept for backwards compatibility
+ */
+export function crossoverPairsToDAG(
+  pairs: CrossoverPair[],
+  queryA: string,
+  queryB: string
+): AttributeDAG {
+  // Group pairs by crossType
+  const pairsByType = pairs.reduce((acc, pair) => {
+    if (!acc[pair.crossType]) acc[pair.crossType] = [];
+    acc[pair.crossType].push(pair);
+    return acc;
+  }, {} as Record<string, CrossoverPair[]>);
+
+  // Create categories from crossTypes
+  const categories: CategoryDefinition[] = Object.keys(pairsByType).map((crossType, index) => ({
+    name: formatCrossTypeName(crossType),
+    description: getCrossTypeDescription(crossType),
+    is_fixed: false,
+    order: index,
+  }));
+
+  // Create nodes from pairs
+  const nodes: DAGNode[] = [];
+  let nodeIndex = 0;
+
+  for (const [crossType, typePairs] of Object.entries(pairsByType)) {
+    const categoryName = formatCrossTypeName(crossType);
+
+    for (const pair of typePairs) {
+      nodes.push({
+        id: `crossover-${nodeIndex}`,
+        name: `${pair.sourceNode.name} × ${pair.targetNode.name}`,
+        category: categoryName,
+        order: nodeIndex,
+      });
+      nodeIndex++;
+    }
+  }
+
+  return {
+    query: `${queryA} × ${queryB}`,
+    categories,
+    nodes,
+    edges: [],
+  };
+}
+
+function formatCrossTypeName(crossType: string): string {
+  if (crossType.startsWith('same-')) {
+    const category = crossType.replace('same-', '');
+    return `Same: ${category}`;
+  }
+  if (crossType.startsWith('cross-')) {
+    const parts = crossType.replace('cross-', '').split('-');
+    if (parts.length >= 2) {
+      return `Cross: ${parts[0]} × ${parts.slice(1).join('-')}`;
+    }
+  }
+  return crossType;
+}
+
+function getCrossTypeDescription(crossType: string): string {
+  if (crossType.startsWith('same-')) {
+    const category = crossType.replace('same-', '');
+    return `Crossover pairs from the same category: ${category}`;
+  }
+  if (crossType.startsWith('cross-')) {
+    return `Crossover pairs from different categories`;
+  }
+  return `Crossover pairs`;
+}

research/README.md

@@ -0,0 +1,38 @@
+# Research: Expert-Augmented LLM Ideation
+
+This folder contains research materials for the academic paper on the novelty-seeking system.
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `literature_review.md` | Comprehensive literature review covering semantic distance theory, conceptual blending, design fixation, LLM limitations, and related work |
+| `references.md` | 55+ academic references with links to papers |
+| `theoretical_framework.md` | The "Semantic Gravity" theoretical model and testable hypotheses |
+| `paper_outline.md` | Complete paper structure, experimental design, and target venues |
+
+## Key Theoretical Contribution
+
+**"Semantic Gravity"**: LLMs exhibit a tendency to generate outputs clustered around high-probability regions of their training distribution, limiting creative novelty. Expert perspectives provide "escape velocity" to break free from this gravity.
+
+## Core Hypotheses
+
+1. **H1**: Multi-expert generation → higher semantic diversity
+2. **H2**: Multi-expert generation → lower patent overlap (higher novelty)
+3. **H3**: Diversity increases with expert count (diminishing returns ~4-6)
+4. **H4**: Expert source affects unconventionality of ideas
+
+## Target Venues
+
+- **CHI** (ACM Conference on Human Factors in Computing Systems)
+- **CSCW** (ACM Conference on Computer-Supported Cooperative Work)
+- **Creativity & Cognition** (ACM Conference)
+- **IJHCS** (International Journal of Human-Computer Studies)
+
+## Next Steps
+
+1. Design concrete experiment protocol
+2. Add measurement code to existing system
+3. Collect experimental data
+4. Conduct human evaluation
+5. Write and submit paper

research/experimental_protocol.md

@@ -0,0 +1,555 @@
+# Experimental Protocol: Expert-Augmented LLM Ideation
+
+## Executive Summary
+
+This document outlines a comprehensive experimental design to test the hypothesis that multi-expert LLM-based ideation produces more diverse and novel ideas than direct LLM generation.
+
+---
+
+## 1. Research Questions
+
+| ID | Research Question |
+|----|-------------------|
+| **RQ1** | Does multi-expert generation produce higher semantic diversity than direct LLM generation? |
+| **RQ2** | Does multi-expert generation produce ideas with lower patent overlap (higher novelty)? |
+| **RQ3** | What is the optimal number of experts for maximizing diversity? |
+| **RQ4** | How do different expert sources (LLM vs Curated vs DBpedia) affect idea quality? |
+| **RQ5** | Does structured attribute decomposition enhance the multi-expert effect? |
+
+---
+
+## 2. Experimental Design Overview
+
+### 2.1 Design Type
+**Mixed Design**: Between-subjects for main conditions × Within-subjects for queries
+
+### 2.2 Variables
+
+#### Independent Variables (Manipulated)
+
+| Variable | Levels | Your System Parameter |
+|----------|--------|----------------------|
+| **Generation Method** | 5 levels (see conditions) | Condition-dependent |
+| **Expert Count** | 1, 2, 4, 6, 8 | `expert_count` |
+| **Expert Source** | LLM, Curated, DBpedia | `expert_source` |
+| **Attribute Structure** | With/Without decomposition | Pipeline inclusion |
+
+#### Dependent Variables (Measured)
+
+| Variable | Measurement Method |
+|----------|-------------------|
+| **Semantic Diversity** | Mean pairwise cosine distance (embeddings) |
+| **Cluster Spread** | Number of clusters, silhouette score |
+| **Patent Novelty** | 1 - (ideas with patent match / total ideas) |
+| **Semantic Distance** | Distance from query centroid |
+| **Human Novelty Rating** | 7-point Likert scale |
+| **Human Usefulness Rating** | 7-point Likert scale |
+| **Human Creativity Rating** | 7-point Likert scale |
+
+#### Control Variables (Held Constant)
+
+| Variable | Fixed Value |
+|----------|-------------|
+| LLM Model | Qwen3:8b (or specify) |
+| Temperature | 0.7 |
+| Total Ideas per Query | 20 |
+| Keywords per Expert | 1 |
+| Deduplication | Disabled for raw comparison |
+| Language | English (for patent search) |
+
+---
+
+## 3. Experimental Conditions
+
+### 3.1 Main Study: Generation Method Comparison
+
+| Condition | Description | Implementation |
+|-----------|-------------|----------------|
+| **C1: Direct** | Direct LLM generation | Prompt: "Generate 20 creative ideas for [query]" |
+| **C2: Single-Expert** | 1 expert × 20 ideas | `expert_count=1`, `keywords_per_expert=20` |
+| **C3: Multi-Expert-4** | 4 experts × 5 ideas each | `expert_count=4`, `keywords_per_expert=5` |
+| **C4: Multi-Expert-8** | 8 experts × 2-3 ideas each | `expert_count=8`, `keywords_per_expert=2-3` |
+| **C5: Random-Perspective** | 4 random words as "perspectives" | Custom prompt with random nouns |
+
+### 3.2 Expert Count Study
+
+| Condition | Expert Count | Ideas per Expert |
+|-----------|--------------|------------------|
+| **E1** | 1 | 20 |
+| **E2** | 2 | 10 |
+| **E4** | 4 | 5 |
+| **E6** | 6 | 3-4 |
+| **E8** | 8 | 2-3 |
+
+### 3.3 Expert Source Study
+
+| Condition | Source | Implementation |
+|-----------|--------|----------------|
+| **S-LLM** | LLM-generated | `expert_source=ExpertSource.LLM` |
+| **S-Curated** | Curated 210 occupations | `expert_source=ExpertSource.CURATED` |
+| **S-DBpedia** | DBpedia 2164 occupations | `expert_source=ExpertSource.DBPEDIA` |
+| **S-Random** | Random word "experts" | Custom implementation |
+
+---
+
+## 4. Query Dataset
+
+### 4.1 Design Principles
+- **Diversity**: Cover multiple domains (consumer products, technology, services, abstract concepts)
+- **Complexity Variation**: Simple objects to complex systems
+- **Familiarity Variation**: Common items to specialized equipment
+- **Cultural Neutrality**: Concepts understandable across cultures
+
+### 4.2 Query Set (30 Queries)
+
+#### Category A: Everyday Objects (10)
+| ID | Query | Complexity |
+|----|-------|------------|
+| A1 | Chair | Low |
+| A2 | Umbrella | Low |
+| A3 | Backpack | Low |
+| A4 | Coffee mug | Low |
+| A5 | Bicycle | Medium |
+| A6 | Refrigerator | Medium |
+| A7 | Smartphone | Medium |
+| A8 | Running shoes | Medium |
+| A9 | Kitchen knife | Low |
+| A10 | Desk lamp | Low |
+
+#### Category B: Technology & Tools (10)
+| ID | Query | Complexity |
+|----|-------|------------|
+| B1 | Solar panel | Medium |
+| B2 | Electric vehicle | High |
+| B3 | 3D printer | High |
+| B4 | Drone | Medium |
+| B5 | Smart thermostat | Medium |
+| B6 | Noise-canceling headphones | Medium |
+| B7 | Water purifier | Medium |
+| B8 | Wind turbine | High |
+| B9 | Robotic vacuum | Medium |
+| B10 | Wearable fitness tracker | Medium |
+
+#### Category C: Services & Systems (10)
+| ID | Query | Complexity |
+|----|-------|------------|
+| C1 | Food delivery service | Medium |
+| C2 | Online education platform | High |
+| C3 | Healthcare appointment system | High |
+| C4 | Public transportation | High |
+| C5 | Hotel booking system | Medium |
+| C6 | Personal finance app | Medium |
+| C7 | Grocery shopping experience | Medium |
+| C8 | Parking solution | Medium |
+| C9 | Elderly care service | High |
+| C10 | Waste management system | High |
+
+### 4.3 Sample Size Justification
+
+Based on [CHI meta-study on effect sizes](https://dl.acm.org/doi/10.1145/3706598.3713671):
+
+- **Queries**: 30 (crossed with conditions)
+- **Expected effect size**: d = 0.5 (medium)
+- **Power target**: 80%
+- **For automatic metrics**: 30 queries × 5 conditions × 20 ideas = 3,000 ideas
+- **For human evaluation**: Subset of 10 queries × 3 conditions × 20 ideas = 600 ideas
+
+---
+
+## 5. Automatic Metrics Collection
+
+### 5.1 Semantic Diversity Metrics
+
+#### 5.1.1 Mean Pairwise Distance (Primary)
+```python
+def compute_mean_pairwise_distance(ideas: List[str], embedding_model: str) -> float:
+    """
+    Compute mean cosine distance between all idea pairs.
+    Higher = more diverse.
+    """
+    embeddings = get_embeddings(ideas, model=embedding_model)
+    n = len(embeddings)
+    distances = []
+    for i in range(n):
+        for j in range(i+1, n):
+            dist = 1 - cosine_similarity(embeddings[i], embeddings[j])
+            distances.append(dist)
+    return np.mean(distances), np.std(distances)
+```
+
+#### 5.1.2 Cluster Analysis
+```python
+def compute_cluster_metrics(ideas: List[str], embedding_model: str) -> dict:
+    """
+    Analyze idea clustering patterns.
+    """
+    embeddings = get_embeddings(ideas, model=embedding_model)
+
+    # Find optimal k using silhouette score
+    silhouette_scores = []
+    for k in range(2, min(len(ideas), 10)):
+        kmeans = KMeans(n_clusters=k)
+        labels = kmeans.fit_predict(embeddings)
+        score = silhouette_score(embeddings, labels)
+        silhouette_scores.append((k, score))
+
+    best_k = max(silhouette_scores, key=lambda x: x[1])[0]
+
+    return {
+        'optimal_clusters': best_k,
+        'silhouette_score': max(silhouette_scores, key=lambda x: x[1])[1],
+        'cluster_distribution': compute_cluster_sizes(embeddings, best_k)
+    }
+```
+
+#### 5.1.3 Semantic Distance from Query
+```python
+def compute_query_distance(query: str, ideas: List[str], embedding_model: str) -> dict:
+    """
+    Measure how far ideas are from the original query.
+    Higher = more novel/distant.
+    """
+    query_emb = get_embedding(query, model=embedding_model)
+    idea_embs = get_embeddings(ideas, model=embedding_model)
+
+    distances = [1 - cosine_similarity(query_emb, e) for e in idea_embs]
+
+    return {
+        'mean_distance': np.mean(distances),
+        'max_distance': np.max(distances),
+        'min_distance': np.min(distances),
+        'std_distance': np.std(distances)
+    }
+```
+
+### 5.2 Patent Novelty Metrics
+
+#### 5.2.1 Patent Overlap Rate
+```python
+def compute_patent_novelty(ideas: List[str], query: str) -> dict:
+    """
+    Search patents for each idea and compute overlap rate.
+    Uses existing patent_search_service.
+    """
+    matches = 0
+    match_details = []
+
+    for idea in ideas:
+        result = patent_search_service.search(idea)
+        if result.has_match:
+            matches += 1
+            match_details.append({
+                'idea': idea,
+                'patent': result.best_match
+            })
+
+    return {
+        'novelty_rate': 1 - (matches / len(ideas)),
+        'match_count': matches,
+        'total_ideas': len(ideas),
+        'match_details': match_details
+    }
+```
+
+### 5.3 Metrics Summary Table
+
+| Metric | Formula | Interpretation |
+|--------|---------|----------------|
+| **Mean Pairwise Distance** | avg(1 - cos_sim(i, j)) for all pairs | Higher = more diverse |
+| **Silhouette Score** | Cluster cohesion vs separation | Higher = clearer clusters |
+| **Optimal Cluster Count** | argmax(silhouette) | More clusters = more themes |
+| **Query Distance** | 1 - cos_sim(query, idea) | Higher = farther from original |
+| **Patent Novelty Rate** | 1 - (matches / total) | Higher = more novel |
+
+---
+
+## 6. Human Evaluation Protocol
+
+### 6.1 Participants
+
+#### 6.1.1 Recruitment
+- **Platform**: Prolific, MTurk, or domain experts
+- **Sample Size**: 60 evaluators (20 per condition group)
+- **Criteria**:
+  - Native English speakers
+  - Bachelor's degree or higher
+  - Attention check pass rate > 80%
+
+#### 6.1.2 Compensation
+- $15/hour equivalent
+- ~30 minutes per session
+- Bonus for high-quality ratings
+
+### 6.2 Rating Scales
+
+#### 6.2.1 Novelty (7-point Likert)
+```
+How novel/surprising is this idea?
+1 = Not at all novel (very common/obvious)
+4 = Moderately novel
+7 = Extremely novel (never seen before)
+```
+
+#### 6.2.2 Usefulness (7-point Likert)
+```
+How useful/practical is this idea?
+1 = Not at all useful (impractical)
+4 = Moderately useful
+7 = Extremely useful (highly practical)
+```
+
+#### 6.2.3 Creativity (7-point Likert)
+```
+How creative is this idea overall?
+1 = Not at all creative
+4 = Moderately creative
+7 = Extremely creative
+```
+
+### 6.3 Procedure
+
+1. **Introduction** (5 min)
+   - Study purpose (without revealing hypotheses)
+   - Rating scale explanation
+   - Practice with 3 example ideas
+
+2. **Training** (5 min)
+   - Rate 5 calibration ideas with feedback
+   - Discuss edge cases
+
+3. **Main Evaluation** (20 min)
+   - Rate 30 ideas (randomized order)
+   - 3 attention check items embedded
+   - Break after 15 ideas
+
+4. **Debriefing** (2 min)
+   - Demographics
+   - Open-ended feedback
+
+### 6.4 Quality Control
+
+| Check | Threshold | Action |
+|-------|-----------|--------|
+| Attention checks | < 2/3 correct | Exclude |
+| Completion time | < 10 min | Flag for review |
+| Variance in ratings | All same score | Exclude |
+| Inter-rater reliability | Cronbach's α < 0.7 | Review ratings |
+
+### 6.5 Analysis Plan
+
+#### 6.5.1 Reliability
+- Cronbach's alpha for each scale
+- ICC (Intraclass Correlation) for inter-rater agreement
+
+#### 6.5.2 Main Analysis
+- Mixed-effects ANOVA: Condition × Query
+- Post-hoc: Tukey HSD for pairwise comparisons
+- Effect sizes: Cohen's d
+
+#### 6.5.3 Correlation with Automatic Metrics
+- Pearson correlation: Human ratings vs semantic diversity
+- Regression: Predict human ratings from automatic metrics
+
+---
+
+## 7. Experimental Procedure
+
+### 7.1 Phase 1: Idea Generation
+
+```
+For each query Q in QuerySet:
+    For each condition C in Conditions:
+
+        If C == "Direct":
+            ideas = direct_llm_generation(Q, n=20)
+
+        Elif C == "Single-Expert":
+            expert = generate_expert(Q, n=1)
+            ideas = expert_transformation(Q, expert, ideas_per_expert=20)
+
+        Elif C == "Multi-Expert-4":
+            experts = generate_experts(Q, n=4)
+            ideas = expert_transformation(Q, experts, ideas_per_expert=5)
+
+        Elif C == "Multi-Expert-8":
+            experts = generate_experts(Q, n=8)
+            ideas = expert_transformation(Q, experts, ideas_per_expert=2-3)
+
+        Elif C == "Random-Perspective":
+            perspectives = random.sample(RANDOM_WORDS, 4)
+            ideas = perspective_generation(Q, perspectives, ideas_per=5)
+
+        Store(Q, C, ideas)
+```
+
+### 7.2 Phase 2: Automatic Metrics
+
+```
+For each (Q, C, ideas) in Results:
+    metrics = {
+        'diversity': compute_mean_pairwise_distance(ideas),
+        'clusters': compute_cluster_metrics(ideas),
+        'query_distance': compute_query_distance(Q, ideas),
+        'patent_novelty': compute_patent_novelty(ideas, Q)
+    }
+    Store(Q, C, metrics)
+```
+
+### 7.3 Phase 3: Human Evaluation
+
+```
+# Sample selection
+selected_queries = random.sample(QuerySet, 10)
+selected_conditions = ["Direct", "Multi-Expert-4", "Multi-Expert-8"]
+
+# Create evaluation set
+evaluation_items = []
+For each Q in selected_queries:
+    For each C in selected_conditions:
+        ideas = Get(Q, C)
+        For each idea in ideas:
+            evaluation_items.append((Q, C, idea))
+
+# Randomize and assign to evaluators
+random.shuffle(evaluation_items)
+assignments = assign_to_evaluators(evaluation_items, n_evaluators=60)
+
+# Collect ratings
+ratings = collect_human_ratings(assignments)
+```
+
+### 7.4 Phase 4: Analysis
+
+```
+# Automatic metrics analysis
+Run ANOVA: diversity ~ condition + query + condition:query
+Run post-hoc: Tukey HSD for condition pairs
+Compute effect sizes
+
+# Human ratings analysis
+Check reliability: Cronbach's alpha, ICC
+Run mixed-effects model: rating ~ condition + (1|evaluator) + (1|query)
+Compute correlations: human vs automatic metrics
+
+# Visualization
+Plot: Diversity by condition (box plots)
+Plot: t-SNE of idea embeddings colored by condition
+Plot: Expert count vs diversity curve
+```
+
+---
+
+## 8. Implementation Checklist
+
+### 8.1 Code to Implement
+
+- [ ] `experiments/generate_ideas.py` - Idea generation for all conditions
+- [ ] `experiments/compute_metrics.py` - Automatic metric computation
+- [ ] `experiments/export_for_evaluation.py` - Prepare human evaluation set
+- [ ] `experiments/analyze_results.py` - Statistical analysis
+- [ ] `experiments/visualize.py` - Generate figures
+
+### 8.2 Data Files to Create
+
+- [ ] `data/queries.json` - 30 queries with metadata
+- [ ] `data/random_words.json` - Random perspective words
+- [ ] `data/generated_ideas/` - Raw idea outputs
+- [ ] `data/metrics/` - Computed metric results
+- [ ] `data/human_ratings/` - Collected ratings
+
+### 8.3 Analysis Outputs
+
+- [ ] `results/diversity_by_condition.csv`
+- [ ] `results/patent_novelty_by_condition.csv`
+- [ ] `results/human_ratings_summary.csv`
+- [ ] `results/statistical_tests.txt`
+- [ ] `figures/` - All visualizations
+
+---
+
+## 9. Expected Results & Hypotheses
+
+### 9.1 Primary Hypotheses
+
+| Hypothesis | Prediction | Metric |
+|------------|------------|--------|
+| **H1** | Multi-Expert-4 > Single-Expert > Direct | Semantic diversity |
+| **H2** | Multi-Expert-8 ≈ Multi-Expert-4 (diminishing returns) | Semantic diversity |
+| **H3** | Multi-Expert > Direct | Patent novelty rate |
+| **H4** | LLM experts > Curated > DBpedia | Unconventionality |
+| **H5** | With attributes > Without attributes | Overall diversity |
+
+### 9.2 Expected Effect Sizes
+
+Based on related work:
+- Diversity increase: d = 0.5-0.8 (medium to large)
+- Patent novelty increase: 20-40% improvement
+- Human creativity rating: d = 0.3-0.5 (small to medium)
+
+### 9.3 Potential Confounds
+
+| Confound | Mitigation |
+|----------|-----------|
+| Query difficulty | Crossed design (all queries × all conditions) |
+| LLM variability | Multiple runs, fixed seed where possible |
+| Evaluator bias | Randomized presentation, blinding |
+| Order effects | Counterbalancing in human evaluation |
+
+---
+
+## 10. Timeline
+
+| Week | Activity |
+|------|----------|
+| 1-2 | Implement idea generation scripts |
+| 3 | Generate all ideas (5 conditions × 30 queries) |
+| 4 | Compute automatic metrics |
+| 5 | Design and pilot human evaluation |
+| 6-7 | Run human evaluation (60 participants) |
+| 8 | Analyze results |
+| 9-10 | Write paper |
+| 11 | Internal review |
+| 12 | Submit |
+
+---
+
+## 11. Appendix: Direct Generation Prompt
+
+For baseline condition C1 (Direct LLM generation):
+
+```
+You are a creative innovation consultant. Generate 20 unique and creative ideas
+for improving or reimagining a [QUERY].
+
+Requirements:
+- Each idea should be distinct and novel
+- Ideas should range from incremental improvements to radical innovations
+- Consider different aspects: materials, functions, user experiences, contexts
+- Provide a brief (15-30 word) description for each idea
+
+Output format:
+1. [Idea keyword]: [Description]
+2. [Idea keyword]: [Description]
+...
+20. [Idea keyword]: [Description]
+```
+
+---
+
+## 12. Appendix: Random Perspective Words
+
+For condition C5 (Random-Perspective), sample from:
+
+```json
+[
+  "ocean", "mountain", "forest", "desert", "cave",
+  "microscope", "telescope", "kaleidoscope", "prism", "lens",
+  "butterfly", "elephant", "octopus", "eagle", "ant",
+  "sunrise", "thunderstorm", "rainbow", "fog", "aurora",
+  "clockwork", "origami", "mosaic", "symphony", "ballet",
+  "ancient", "futuristic", "organic", "crystalline", "liquid",
+  "whisper", "explosion", "rhythm", "silence", "echo"
+]
+```
+
+This tests whether ANY perspective shift helps, or if EXPERT perspectives specifically matter.

research/literature_review.md

@@ -0,0 +1,209 @@
+# Literature Review: Expert-Augmented LLM Ideation
+
+## 1. Core Directly-Related Work
+
+### 1.1 Wisdom of Crowds via Role Assumption
+**Bringing the Wisdom of the Crowd to an Individual by Having the Individual Assume Different Roles** (ACM C&C 2017)
+
+Groups of people tend to generate more diverse ideas than individuals because each group member brings a different perspective. This study showed it's possible to help individuals think more like a group by asking them to approach a problem from different perspectives. In an experiment with 54 crowd workers, participants who assumed different expert roles came up with more creative ideas than those who did not.
+
+**Gap for our work**: This was human-based role-playing. Our system automates this with LLM-powered expert perspectives.
+
+### 1.2 PersonaFlow: LLM-Simulated Expert Perspectives
+**PersonaFlow: Designing LLM-Simulated Expert Perspectives for Enhanced Research Ideation** (2024)
+
+PersonaFlow provides multiple perspectives by using LLMs to simulate domain-specific experts. User studies showed it increased the perceived relevance and creativity of ideated research directions and promoted users' critical thinking activities without increasing perceived cognitive load.
+
+**Gap for our work**: PersonaFlow focuses on research ideation. Our system applies to product/innovation ideation with structured attribute decomposition.
+
+### 1.3 PopBlends: Conceptual Blending with LLMs
+**PopBlends: Strategies for Conceptual Blending with Large Language Models** (CHI 2023)
+
+PopBlends automatically suggests conceptual blends using both traditional knowledge extraction and LLMs. Studies showed people found twice as many blend suggestions with the system, with half the mental demand.
+
+**Gap for our work**: We structure blending through expert domain knowledge rather than direct concept pairing.
+
+### 1.4 BILLY: Persona Vector Merging
+**BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation** (2025)
+
+Proposes fusing persona vectors in activation space to steer LLM output towards multiple perspectives simultaneously, requiring only a single additive operation during inference.
+
+**Gap for our work**: We use sequential multi-expert generation rather than vector fusion, allowing more explicit control and interpretability.
+
+---
+
+## 2. Theoretical Foundations
+
+### 2.1 Semantic Distance Theory
+
+**Core Insight** (Mednick, 1962): Creative thinking involves connecting weakly related, remote concepts in semantic memory. The farther one "moves away" from a conventional idea, the more creative the new idea will likely be.
+
+**Key Research**:
+- Semantic distance plays an important role in the creative process
+- A more "flexible" semantic memory structure (higher connectivity, shorter distances) facilitates creative idea generation
+- Quantitative measures using LSA and semantic networks can objectively examine creative output
+- Divergent Semantic Integration (DSI) correlates strongly with human creativity ratings (72% variance explained)
+
+**Application to Our Work**: Expert perspectives force semantic "jumps" to distant domains that LLMs wouldn't naturally traverse.
+
+```
+Without Expert: "Chair" → furniture, sitting, comfort (short semantic distance)
+With Expert:    "Chair" + Marine Biologist → pressure, buoyancy, coral (long semantic distance)
+```
+
+### 2.2 Conceptual Blending Theory
+
+**Core Insight** (Fauconnier & Turner, 2002): Creative products emerge from blending elements of two input spaces into a novel integrated space.
+
+**Key Research**:
+- Blending process: (1) find connecting concept between inputs, (2) map elements that can be blended
+- Generative AI demonstrates ability to blend and integrate concepts (bisociation)
+- Trisociation (three-concept blending) is being used for AI-augmented idea generation
+- Conceptual blending provides terminology for describing creative products
+
+**Limitation**: Blending theory doesn't explain where inputs originate - the "inspiration problem."
+
+**Application to Our Work**: Each expert provides a distinct "input space" enabling systematic multi-space blending. Our attribute decomposition provides structured inputs for blending.
+
+### 2.3 Design Fixation
+
+**Core Insight** (Jansson & Smith, 1991): Design fixation is "blind adherence to a set of ideas or concepts limiting the output of conceptual design."
+
+**Key Research**:
+- Fixation results from categorical knowledge organization around prototypes
+- Accessing prototypes requires less cognitive effort than processing exemplars
+- Diverse teams, model-making, and facilitation help prevent fixation
+- Reflecting on prior fixation episodes is most effective prevention
+
+**Neural Evidence**: fMRI studies show distinct patterns during fixated vs. creative ideation.
+
+**Application to Our Work**: LLMs exhibit "semantic fixation" on high-probability outputs. Expert perspectives break this by forcing activation of non-prototype knowledge.
+
+### 2.4 Constraint-Based Creativity
+
+**Core Insight**: Paradoxically, constraints can enhance creativity by pushing beyond the path of least resistance.
+
+**Key Research**:
+- Constraints push people to search for more distant ideas in semantic memory
+- Extreme constraints may require different types of creative problem-solving
+- Not all constraints promote creativity for all individuals/tasks
+- A "constraint-leveraging mindset" can be developed through experience
+
+**Application to Our Work**: Expert role = productive constraint that expands rather than limits creative space. The expert perspective forces exploration of non-obvious solution spaces.
+
+---
+
+## 3. LLM Limitations in Creative Generation
+
+### 3.1 Design Fixation from AI
+**The Effects of Generative AI on Design Fixation and Divergent Thinking** (CHI 2024)
+
+Key finding: AI exposure during ideation leads to HIGHER fixation. Participants who used AI produced:
+- Fewer ideas
+- Less variety
+- Lower originality
+
+compared to baseline (no AI assistance).
+
+### 3.2 Dual Mechanisms: Inspiration vs. Fixation
+**Inspiration Booster or Creative Fixation?** (Nature Humanities & Social Sciences, 2025)
+
+- LLMs help in **simple** creative tasks (inspiration stimulation)
+- LLMs **hurt** in **complex** creative tasks (creative fixation)
+
+**Application to Our Work**: Our structured decomposition manages complexity, while multi-expert approach maintains inspiration benefits.
+
+### 3.3 Statistical Pattern Perpetuation
+**Bias and Fairness in Large Language Models: A Survey** (MIT Press, 2024)
+
+LLMs learn, perpetuate, and amplify patterns from training data. This applies to creative outputs - LLMs generate what is statistically common/expected.
+
+### 3.4 Generalization Bias
+**Generalization Bias in LLM Summarization** (Royal Society, 2025)
+
+LLMs' overgeneralization tendency produces outputs that lack sufficient empirical support. This suggests a bias toward "safe" middle-ground outputs rather than novel extremes.
+
+---
+
+## 4. Role-Playing and Perspective-Taking
+
+### 4.1 Creativity Enhancement
+Research on tabletop role-playing games (TTRPGs) demonstrates:
+- Significant positive impact on creativity potential through divergent thinking
+- TTRPG players exhibit significantly higher creativity than non-players
+- Perspective-taking is closely linked to empathy and cognitive flexibility
+
+### 4.2 Therapeutic and Educational Applications
+- Role-playing develops perspective-taking, storytelling, creativity, and self-expression
+- Physiological, emotional, and mental well-being from play enables creative ideation
+- Play signals psychological safety, which is essential for creativity
+
+### 4.3 Design Research Applications
+- Role-playing stimulates creativity by exploring alternative solutions
+- Offers safe environment to explore failure modes and challenge assumptions
+- Well-suited for early-stage ideation and empathy-critical moments
+
+---
+
+## 5. Creativity Support Tools (CSTs)
+
+### 5.1 Current State
+- CSTs primarily support **divergent** thinking
+- **Convergent** thinking often neglected
+- Ideal CST should offer tailored support for both
+
+### 5.2 AI as Creative Partner
+- Collaborative ideation systems expose users to different ideas
+- Competing theories on when/whether such exposure helps
+- Tool-mediated expert activity view: computers as "mediating artifacts people act through"
+
+### 5.3 Evaluation Methods
+**Consensual Assessment Technique (CAT)**:
+- Pool of experts independently evaluate artifacts
+- Creative if high evaluations + high interrater reliability (Cronbach's alpha > 0.7)
+
+**Semantic Distance Measures**:
+- SemDis platform for automated creativity assessment
+- Overcomes labor cost and subjectivity of human rating
+- Uses NLP to quantify semantic relatedness
+
+---
+
+## 6. Our Theoretical Contribution
+
+### The "Semantic Gravity" Problem
+
+```
+Direct LLM Generation:
+  P(idea | query)
+  → Samples from high-probability region
+  → Ideas cluster around training distribution modes
+  → "Semantic gravity" pulls toward conventional associations
+```
+
+### Expert Transformation Solution
+
+```
+Conditioned Generation:
+  P(idea | query, expert)
+  → Expert perspective activates distant semantic regions
+  → Forces conceptual blending across domains
+  → Breaks design fixation through productive constraints
+```
+
+### Multi-Expert Aggregation
+
+```
+Diverse Experts → Semantic Coverage
+  → "Inner crowd" wisdom without actual crowd
+  → Systematic exploration of idea space
+  → Deduplication ensures non-redundant novelty
+```
+
+### Theoretical Model
+
+1. **Attribute Decomposition**: Structures the problem space (categories, attributes)
+2. **Expert Perspectives**: Forces semantic jumps to distant domains
+3. **Multi-Expert Aggregation**: Achieves crowd-like diversity individually
+4. **Deduplication**: Ensures generated ideas are truly distinct
+5. **Patent Validation**: Grounds novelty in real-world uniqueness

research/paper_outline.md

@@ -0,0 +1,288 @@
+# Paper Outline: Expert-Augmented LLM Ideation
+
+## Suggested Titles
+
+1. **"Breaking Semantic Gravity: Expert-Augmented LLM Ideation for Enhanced Creativity"**
+2. "Beyond Interpolation: Multi-Expert Perspectives for Combinatorial Innovation"
+3. "Escaping the Relevance Trap: Structured Expert Frameworks for Creative AI"
+4. "From Crowd to Expert: Simulating Diverse Perspectives for LLM-Based Ideation"
+
+---
+
+## Abstract (Draft)
+
+Large Language Models (LLMs) are increasingly used for creative ideation, yet they exhibit a phenomenon we term "semantic gravity" - the tendency to generate outputs clustered around high-probability regions of their training distribution. This limits the novelty and diversity of generated ideas. We propose a multi-expert transformation framework that systematically activates diverse semantic regions by conditioning LLM generation on simulated expert perspectives. Our system decomposes concepts into structured attributes, generates ideas through multiple domain-expert viewpoints, and employs semantic deduplication to ensure genuine diversity. Through experiments comparing multi-expert generation against direct LLM generation and single-expert baselines, we demonstrate that our approach produces ideas with [X]% higher semantic diversity and [Y]% lower patent overlap. We contribute a theoretical framework explaining LLM creativity limitations and an open-source system for innovation ideation.
+
+---
+
+## 1. Introduction
+
+### 1.1 The Promise and Problem of LLM Creativity
+- LLMs widely adopted for creative tasks
+- Initial enthusiasm: infinite idea generation
+- Emerging concern: quality and diversity issues
+
+### 1.2 The Semantic Gravity Problem
+- Define the phenomenon
+- Why it occurs (statistical learning, mode collapse)
+- Why it matters (innovation requires novelty)
+
+### 1.3 Our Solution: Expert-Augmented Ideation
+- Brief overview of the approach
+- Key insight: expert perspectives as semantic "escape velocity"
+- Contributions preview
+
+### 1.4 Paper Organization
+- Roadmap for the rest of the paper
+
+---
+
+## 2. Related Work
+
+### 2.1 Theoretical Foundations
+- Semantic distance and creativity (Mednick, 1962)
+- Conceptual blending theory (Fauconnier & Turner)
+- Design fixation (Jansson & Smith)
+- Constraint-based creativity
+
+### 2.2 LLM Limitations in Creative Generation
+- Design fixation from AI (CHI 2024)
+- Dual mechanisms: inspiration vs. fixation
+- Bias and pattern perpetuation
+
+### 2.3 Persona-Based Prompting
+- PersonaFlow (2024)
+- BILLY persona vectors (2025)
+- Quantifying persona effects (ACL 2024)
+
+### 2.4 Creativity Support Tools
+- Wisdom of crowds approaches
+- Human-AI collaboration in ideation
+- Evaluation methods (CAT, semantic distance)
+
+### 2.5 Positioning Our Work
+- Gap: No end-to-end system combining structured decomposition + multi-expert transformation + deduplication
+- Distinction from PersonaFlow: product innovation focus, attribute structure
+
+---
+
+## 3. System Design
+
+### 3.1 Overview
+- Pipeline diagram
+- Design rationale
+
+### 3.2 Attribute Decomposition
+- Category analysis (dynamic vs. fixed)
+- Attribute generation per category
+- DAG relationship mapping
+
+### 3.3 Expert Team Generation
+- Expert sources: LLM-generated, curated, external databases
+- Diversity optimization strategies
+- Domain coverage considerations
+
+### 3.4 Expert Transformation
+- Conditioning mechanism
+- Keyword generation
+- Description generation
+- Parallel processing for efficiency
+
+### 3.5 Semantic Deduplication
+- Embedding-based approach
+- LLM-based approach
+- Threshold selection
+
+### 3.6 Novelty Validation
+- Patent search integration
+- Overlap scoring
+
+---
+
+## 4. Experiments
+
+### 4.1 Research Questions
+- RQ1: Does multi-expert generation increase semantic diversity?
+- RQ2: Does multi-expert generation reduce patent overlap?
+- RQ3: What is the optimal number of experts?
+- RQ4: How do expert sources affect output quality?
+
+### 4.2 Experimental Setup
+
+#### 4.2.1 Dataset
+- N concepts/queries for ideation
+- Selection criteria (diverse domains, complexity levels)
+
+#### 4.2.2 Conditions
+| Condition | Description |
+|-----------|-------------|
+| Baseline | Direct LLM: "Generate 20 creative ideas for X" |
+| Single-Expert | 1 expert × 20 ideas |
+| Multi-Expert-4 | 4 experts × 5 ideas each |
+| Multi-Expert-8 | 8 experts × 2-3 ideas each |
+| Random-Perspective | 4 random words as "perspectives" |
+
+#### 4.2.3 Controls
+- Same LLM model (specify version)
+- Same temperature settings
+- Same total idea count per condition
+
+### 4.3 Metrics
+
+#### 4.3.1 Semantic Diversity
+- Mean pairwise cosine distance between embeddings
+- Cluster distribution analysis
+- Silhouette score for idea clustering
+
+#### 4.3.2 Novelty
+- Patent overlap rate
+- Semantic distance from query centroid
+
+#### 4.3.3 Quality (Human Evaluation)
+- Novelty rating (1-7 Likert)
+- Usefulness rating (1-7 Likert)
+- Creativity rating (1-7 Likert)
+- Interrater reliability (Cronbach's alpha)
+
+### 4.4 Procedure
+- Idea generation process
+- Evaluation process
+- Statistical analysis methods
+
+---
+
+## 5. Results
+
+### 5.1 Semantic Diversity (RQ1)
+- Quantitative results
+- Visualization (t-SNE/UMAP of idea embeddings)
+- Statistical significance tests
+
+### 5.2 Patent Novelty (RQ2)
+- Overlap rates by condition
+- Examples of high-novelty ideas
+
+### 5.3 Expert Count Analysis (RQ3)
+- Diversity vs. expert count curve
+- Diminishing returns analysis
+- Optimal expert count recommendation
+
+### 5.4 Expert Source Comparison (RQ4)
+- LLM-generated vs. curated vs. random
+- Unconventionality metrics
+
+### 5.5 Human Evaluation Results
+- Rating distributions
+- Condition comparisons
+- Correlation with automatic metrics
+
+---
+
+## 6. Discussion
+
+### 6.1 Interpreting the Results
+- Why multi-expert works
+- The role of structured decomposition
+- Deduplication importance
+
+### 6.2 Theoretical Implications
+- Semantic gravity as framework for LLM creativity
+- Expert perspectives as productive constraints
+- Inner crowd wisdom
+
+### 6.3 Practical Implications
+- When to use multi-expert approach
+- Expert selection strategies
+- Integration with existing workflows
+
+### 6.4 Limitations
+- LLM-specific results may not generalize
+- Patent overlap as proxy for true novelty
+- Human evaluation subjectivity
+- Single-language experiments
+
+### 6.5 Future Work
+- Cross-cultural creativity
+- Domain-specific expert optimization
+- Real-world deployment studies
+- Integration with other creativity techniques
+
+---
+
+## 7. Conclusion
+
+- Summary of contributions
+- Key takeaways
+- Broader impact
+
+---
+
+## Appendices
+
+### A. Prompt Templates
+- Expert generation prompts
+- Keyword generation prompts
+- Description generation prompts
+
+### B. Full Experimental Results
+- Complete data tables
+- Additional visualizations
+
+### C. Expert Source Details
+- Curated occupation list
+- DBpedia/Wikidata query details
+
+### D. Human Evaluation Protocol
+- Instructions for raters
+- Example ratings
+- Training materials
+
+---
+
+## Target Venues
+
+### Tier 1 (Recommended)
+1. **CHI** - ACM Conference on Human Factors in Computing Systems
+   - Strong fit: creativity support tools, human-AI collaboration
+   - Deadline: typically September
+
+2. **CSCW** - ACM Conference on Computer-Supported Cooperative Work
+   - Good fit: collaborative ideation, crowd wisdom
+   - Deadline: typically April/January
+
+3. **Creativity & Cognition** - ACM Conference
+   - Perfect fit: computational creativity focus
+   - Smaller but specialized venue
+
+### Tier 2 (Alternative)
+4. **DIS** - ACM Designing Interactive Systems
+   - Good fit: design ideation tools
+
+5. **UIST** - ACM Symposium on User Interface Software and Technology
+   - If system/interaction focus emphasized
+
+6. **ICCC** - International Conference on Computational Creativity
+   - Specialized computational creativity venue
+
+### Journal Options
+1. **International Journal of Human-Computer Studies (IJHCS)**
+2. **ACM Transactions on Computer-Human Interaction (TOCHI)**
+3. **Design Studies**
+4. **Creativity Research Journal**
+
+---
+
+## Timeline Checklist
+
+- [ ] Finalize experimental design
+- [ ] Collect/select query dataset
+- [ ] Run all experimental conditions
+- [ ] Compute automatic metrics
+- [ ] Design human evaluation study
+- [ ] Recruit evaluators
+- [ ] Conduct human evaluation
+- [ ] Statistical analysis
+- [ ] Write first draft
+- [ ] Internal review
+- [ ] Revision
+- [ ] Submit

research/references.md

@@ -0,0 +1,208 @@
+# References
+
+## Core Related Work
+
+1. **Siangliulue, P., Arnold, K. C., Gajos, K. Z., & Dow, S. P.** (2017). Bringing the Wisdom of the Crowd to an Individual by Having the Individual Assume Different Roles. *Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition (C&C '17)*, 131-141.
+   - https://dl.acm.org/doi/10.1145/3059454.3059467
+
+2. **Liu, Y., Sharma, A., et al.** (2024). PersonaFlow: Designing LLM-Simulated Expert Perspectives for Enhanced Research Ideation. *arXiv preprint*.
+   - https://arxiv.org/html/2409.12538v1
+   - https://www.semanticscholar.org/paper/PersonaFlow:-Designing-LLM-Simulated-Expert-for-Liu-Sharma/eb0c224be9191e39452f20b2cbb886b5ecc4f57b
+
+3. **Choi, J., et al.** (2023). PopBlends: Strategies for Conceptual Blending with Large Language Models. *Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems*.
+   - https://dl.acm.org/doi/10.1145/3544548.3580948
+
+4. **BILLY Authors** (2025). BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation. *arXiv preprint*.
+   - https://arxiv.org/html/2510.10157v1
+
+---
+
+## Semantic Distance & Creative Cognition
+
+5. **Mednick, S. A.** (1962). The associative basis of the creative process. *Psychological Review, 69*(3), 220-232.
+   - (Classic foundational paper)
+
+6. **Kenett, Y. N., & Faust, M.** (2019). Going the Extra Creative Mile: The Role of Semantic Distance in Creativity – Theory, Research, and Measurement. *The Cambridge Handbook of the Neuroscience of Creativity*.
+   - https://www.cambridge.org/core/books/abs/cambridge-handbook-of-the-neuroscience-of-creativity/going-the-extra-creative-mile-the-role-of-semantic-distance-in-creativity-theory-research-and-measurement/3AD9143E69A463F85F2D8CC8940425CA
+
+7. **Beaty, R. E., & Johnson, D. R.** (2021). Automating creativity assessment with SemDis: An open platform for computing semantic distance. *Behavior Research Methods, 53*, 757-780.
+   - https://link.springer.com/article/10.3758/s13428-020-01453-w
+
+8. **What can quantitative measures of semantic distance tell us about creativity?** (2018). *Current Opinion in Behavioral Sciences*.
+   - https://www.sciencedirect.com/science/article/abs/pii/S2352154618301098
+
+9. **Semantic Memory and Creativity: The Costs and Benefits of Semantic Memory Structure in Generating Original Ideas** (2023). *PMC*.
+   - https://pmc.ncbi.nlm.nih.gov/articles/PMC10128864/
+
+10. **The Role of Semantic Associations as a Metacognitive Cue in Creative Idea Generation** (2023). *PMC*.
+    - https://pmc.ncbi.nlm.nih.gov/articles/PMC10141130/
+
+---
+
+## Conceptual Blending Theory
+
+11. **Fauconnier, G., & Turner, M.** (2002). *The Way We Think: Conceptual Blending and the Mind's Hidden Complexities*. Basic Books.
+
+12. **Conceptual Blending** - Wikipedia Overview
+    - https://en.wikipedia.org/wiki/Conceptual_blending
+
+13. **Pereira, F. C.** (2007). *Creativity and Artificial Intelligence: A Conceptual Blending Approach*. Mouton de Gruyter.
+    - https://dl.acm.org/doi/10.5555/1557446
+    - https://www.researchgate.net/publication/332711522_Creativity_and_Artificial_Intelligence_A_Conceptual_Blending_Approach
+
+14. **Confalonieri, R., et al.** (2018). A computational framework for conceptual blending. *Artificial Intelligence, 256*, 105-129.
+    - https://www.sciencedirect.com/science/article/pii/S000437021730142X
+
+15. **Trisociation with AI for Creative Idea Generation** (2025). *California Management Review*.
+    - https://cmr.berkeley.edu/2025/01/trisociation-with-ai-for-creative-idea-generation/
+
+---
+
+## Design Fixation & Constraint-Based Creativity
+
+16. **Jansson, D. G., & Smith, S. M.** (1991). Design fixation. *Design Studies, 12*(1), 3-11.
+    - (Classic foundational paper)
+
+17. **Design Fixation: A Cognitive Model**. *Design Society*.
+    - https://www.designsociety.org/download-publication/25504/design_fixation_a_cognitive_model
+
+18. **Crilly, N.** (2019). Research Design Fixation. *Cambridge Repository*.
+    - https://www.repository.cam.ac.uk/bitstreams/2c002015-8771-4694-ad48-0e4b52008bdf/download
+
+19. **Using fMRI to deepen our understanding of design fixation** (2020). *Design Science, Cambridge Core*.
+    - https://www.cambridge.org/core/journals/design-science/article/using-fmri-to-deepen-our-understanding-of-design-fixation/2DD81FEE8ED682F6DFF415BF2948EFA6
+
+20. **Acar, O. A., Tarakci, M., & van Knippenberg, D.** (2019). Creativity and Innovation Under Constraints: A Cross-Disciplinary Integrative Review. *Journal of Management, 45*(1), 96-121.
+    - https://journals.sagepub.com/doi/full/10.1177/0149206318805832
+
+21. **Cromwell, J. R.** (2024). How combinations of constraint affect creativity: A new typology of creative problem solving in organizations. *Organizational Psychology Review*.
+    - https://journals.sagepub.com/doi/10.1177/20413866231202031
+
+22. **Creativity from constraints: Theory and applications to education** (2022). *Thinking Skills and Creativity*.
+    - https://www.sciencedirect.com/science/article/abs/pii/S1871187122001870
+
+---
+
+## LLM Limitations in Creative Generation
+
+23. **Wadinambiarachchi, S., et al.** (2024). The Effects of Generative AI on Design Fixation and Divergent Thinking. *Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems*.
+    - https://dl.acm.org/doi/full/10.1145/3613904.3642919
+    - https://arxiv.org/html/2403.11164v1
+
+24. **Inspiration booster or creative fixation? The dual mechanisms of LLMs in shaping individual creativity in tasks of different complexity** (2025). *Humanities and Social Sciences Communications (Nature)*.
+    - https://www.nature.com/articles/s41599-025-05867-9
+
+25. **Gallegos, I. O., et al.** (2024). Bias and Fairness in Large Language Models: A Survey. *Computational Linguistics, 50*(3), 1097-1179. MIT Press.
+    - https://direct.mit.edu/coli/article/50/3/1097/121961/Bias-and-Fairness-in-Large-Language-Models-A
+
+26. **Generalization bias in large language model summarization of scientific research** (2025). *Royal Society Open Science, 12*(4).
+    - https://royalsocietypublishing.org/rsos/article/12/4/241776/235656/Generalization-bias-in-large-language-model
+
+27. **LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Models** (2025). *arXiv*.
+    - https://arxiv.org/html/2505.19240v1
+
+---
+
+## Persona Prompting & Multi-Agent Systems
+
+28. **Quantifying the Persona Effect in LLM Simulations** (2024). *ACL 2024*.
+    - https://aclanthology.org/2024.acl-long.554.pdf
+    - https://www.emergentmind.com/topics/persona-effect-in-llm-simulations
+
+29. **Two Tales of Persona in LLMs: A Survey of Role-Playing** (2024). *EMNLP Findings*.
+    - https://aclanthology.org/2024.findings-emnlp.969.pdf
+
+30. **LLM Generated Persona is a Promise with a Catch** (2024). *Semantic Scholar*.
+    - https://www.semanticscholar.org/paper/LLM-Generated-Persona-is-a-Promise-with-a-Catch-Li-Chen/3ea29481ec11d1568fde727d236f71e44e4e2ad0
+
+31. **Using AI for User Representation: An Analysis of 83 Persona Prompts** (2025). *arXiv*.
+    - https://arxiv.org/html/2508.13047v1
+
+32. **Scaffolding Creativity: How Divergent and Convergent Personas Shape AI-Assisted Ideation** (2025). *arXiv*.
+    - https://arxiv.org/pdf/2510.26490
+
+---
+
+## Role-Playing & Perspective-Taking
+
+33. **Chung, T. S.** (2013). Table-top role playing game and creativity. *Thinking Skills and Creativity, 8*, 56-71.
+    - https://www.researchgate.net/publication/257701334_Table-top_role_playing_game_and_creativity
+
+34. **The effect of tabletop role-playing games on the creative potential and emotional creativity of Taiwanese college students** (2015). *Thinking Skills and Creativity*.
+    - https://www.researchgate.net/publication/284013184_The_effect_of_tabletop_role-playing_games_on_the_creative_potential_and_emotional_creativity_of_Taiwanese_college_students
+
+35. **Psychology and Role-Playing Games** (2019). *ResearchGate*.
+    - https://www.researchgate.net/publication/331758159_Psychology_and_Role-Playing_Games
+
+36. **Role Playing and Perspective Taking: An Educational Point of View** (2020). *ResearchGate*.
+    - https://www.researchgate.net/publication/346610467_Role_Playing_and_Perspective_Taking_An_Educational_Point_of_View
+
+---
+
+## Creativity Support Tools & Evaluation
+
+37. **Jordanous, A.** (2018). Evaluating Computational Creativity: An Interdisciplinary Tutorial. *ACM Computing Surveys, 51*(2), Article 28.
+    - https://dl.acm.org/doi/10.1145/3167476
+
+38. **Evaluating Creativity in Computational Co-Creative Systems** (2018). *ResearchGate*.
+    - https://www.researchgate.net/publication/326646917_Evaluating_Creativity_in_Computational_Co-Creative_Systems
+
+39. **The Intersection of Users, Roles, Interactions, and Technologies in Creativity Support Tools** (2021). *DIS '21*.
+    - https://dl.acm.org/doi/10.1145/3461778.3462050
+
+40. **What Counts as 'Creative' Work? Articulating Four Epistemic Positions in Creativity-Oriented HCI Research** (2024). *CHI '24*.
+    - https://dl.acm.org/doi/10.1145/3613904.3642854
+
+41. **Colton, S., & Wiggins, G. A.** (2012). Computational Creativity: The Final Frontier? *ECAI 2012*.
+    - https://link.springer.com/article/10.1007/s00354-020-00116-w
+
+---
+
+## AI-Augmented Design & Ideation
+
+42. **The effect of AI-based inspiration on human design ideation** (2023). *CoDesign*.
+    - https://www.tandfonline.com/doi/full/10.1080/21650349.2023.2167124
+
+43. **A Hybrid Prototype Method Combining Physical Models and Generative AI to Support Creativity in Conceptual Design** (2024). *ACM TOCHI*.
+    - https://dl.acm.org/doi/10.1145/3689433
+
+44. **Artificial intelligence for design education: A conceptual approach to enhance students' divergent and convergent thinking** (2025). *IJTDE*.
+    - https://link.springer.com/article/10.1007/s10798-025-09964-3
+
+45. **The Ideation Compass: Supporting interdisciplinary creative dialogues with real time visualization** (2022). *CoDesign*.
+    - https://www.tandfonline.com/doi/full/10.1080/21650349.2022.2142674
+
+46. **Guiding data-driven design ideation by knowledge distance** (2021). *Knowledge-Based Systems*.
+    - https://www.sciencedirect.com/science/article/abs/pii/S0950705121001362
+
+---
+
+## CHI/CSCW Related Papers
+
+47. **Chan, J., Dang, S., & Dow, S. P.** (2016). Improving Crowd Innovation with Expert Facilitation. *CSCW '16*.
+
+48. **Koch, J., et al.** (2020). ImageSense: An Intelligent Collaborative Ideation Tool to Support Diverse Human-Computer Partnerships. *CSCW '20*.
+
+49. **Yu, L., Kittur, A., & Kraut, R. E.** (2014). Distributed Analogical Idea Generation: Inventing with Crowds. *CHI '14*.
+
+50. **Crowdboard** (2017). *C&C '17*.
+    - https://dl.acm.org/doi/10.1145/3059454.3059477
+
+51. **Collaborative Creativity** (2011). *CHI '11*.
+    - https://dl.acm.org/doi/10.1145/1978942.1979214
+
+52. **Beyond Automation: How UI/UX Designers Perceive AI as a Creative Partner in the Divergent Thinking Stages** (2025). *arXiv*.
+    - https://arxiv.org/html/2501.18778
+
+---
+
+## Additional Resources
+
+53. **Automatic Scoring of Metaphor Creativity with Large Language Models** (2024). *Creativity Research Journal*.
+    - https://www.tandfonline.com/doi/full/10.1080/10400419.2024.2326343
+
+54. **Wisdom of Crowds** - Surowiecki, J. (2004). *The Wisdom of Crowds*. Doubleday.
+    - https://en.wikipedia.org/wiki/The_Wisdom_of_Crowds
+
+55. **Research: When Used Correctly, LLMs Can Unlock More Creative Ideas** (2025). *Harvard Business Review*.
+    - https://hbr.org/2025/12/research-when-used-correctly-llms-can-unlock-more-creative-ideas

research/theoretical_framework.md

@@ -0,0 +1,280 @@
+# Theoretical Framework: Expert-Augmented LLM Ideation
+
+## The Core Problem: LLM "Semantic Gravity"
+
+### What is Semantic Gravity?
+
+When LLMs generate creative ideas directly, they exhibit a phenomenon we term "semantic gravity" - the tendency to generate outputs that cluster around high-probability regions of their training distribution.
+
+```
+Direct LLM Generation:
+  Input: "Generate creative ideas for a chair"
+
+  LLM Process:
+    P(idea | "chair") → samples from training distribution
+
+  Result:
+    - "Ergonomic office chair" (high probability)
+    - "Foldable portable chair" (high probability)
+    - "Eco-friendly bamboo chair" (moderate probability)
+
+  Problem:
+    → Ideas cluster in predictable semantic neighborhoods
+    → Limited exploration of distant conceptual spaces
+    → "Creative" outputs are interpolations, not extrapolations
+```
+
+### Why Does This Happen?
+
+1. **Statistical Pattern Learning**: LLMs learn co-occurrence patterns from training data
+2. **Mode Collapse**: When asked to be "creative," LLMs sample from the distribution of "creative ideas" they've seen
+3. **Relevance Trap**: Strong associations dominate weak ones (chair→furniture >> chair→marine biology)
+4. **Prototype Bias**: Outputs gravitate toward category prototypes, not edge cases
+
+---
+
+## The Solution: Expert Perspective Transformation
+
+### Theoretical Basis
+
+Our approach draws from three key theoretical foundations:
+
+#### 1. Semantic Distance Theory (Mednick, 1962)
+
+> "Creative thinking involves connecting weakly related, remote concepts in semantic memory."
+
+**Key insight**: Creativity correlates with semantic distance. The farther the conceptual "jump," the more creative the result.
+
+**Our application**: Expert perspectives force semantic jumps that LLMs wouldn't naturally make.
+
+```
+Without Expert:
+  "Chair" → furniture, sitting, comfort, design
+  Semantic distance: SHORT
+
+With Marine Biologist Expert:
+  "Chair" → underwater pressure, coral structure, buoyancy, bioluminescence
+  Semantic distance: LONG
+
+Result: Novel ideas like "pressure-adaptive seating" or "coral-inspired structural support"
+```
+
+#### 2. Conceptual Blending Theory (Fauconnier & Turner, 2002)
+
+> "Creative products emerge from blending elements of two input spaces into a novel integrated space."
+
+**The blending process**:
+1. Input Space 1: The target concept (e.g., "chair")
+2. Input Space 2: The expert's domain knowledge (e.g., marine biology)
+3. Generic Space: Abstract structure shared by both
+4. Blended Space: Novel integration of elements from both inputs
+
+**Our application**: Each expert provides a distinct input space for systematic blending.
+
+```
+┌─────────────────┐     ┌─────────────────┐
+│   Input 1       │     │   Input 2       │
+│   "Chair"       │     │ Marine Biology  │
+│   - support     │     │ - pressure      │
+│   - sitting     │     │ - buoyancy      │
+│   - comfort     │     │ - adaptation    │
+└────────┬────────┘     └────────┬────────┘
+         │                       │
+         └───────────┬───────────┘
+                     ▼
+         ┌─────────────────────┐
+         │   Blended Space     │
+         │   Novel Chair Ideas │
+         │   - pressure-adapt  │
+         │   - buoyant support │
+         │   - bio-adaptive    │
+         └─────────────────────┘
+```
+
+#### 3. Design Fixation Breaking (Jansson & Smith, 1991)
+
+> "Design fixation is blind adherence to initial ideas, limiting creative output."
+
+**Fixation occurs because**:
+- Knowledge is organized around category prototypes
+- Prototypes require less cognitive effort to access
+- Initial examples anchor subsequent ideation
+
+**Our application**: Expert perspectives act as "defixation triggers" by activating non-prototype knowledge.
+
+```
+Without Intervention:
+  Prototype: "standard four-legged chair"
+  Fixation: Variations on four-legged design
+
+With Expert Intervention:
+  Archaeologist: "Ancient people sat differently..."
+  Dance Therapist: "Seating affects movement expression..."
+
+  Fixation Broken: Entirely new seating paradigms explored
+```
+
+---
+
+## The Multi-Expert Aggregation Model
+
+### From "Wisdom of Crowds" to "Inner Crowd"
+
+Research shows that groups generate more diverse ideas because each member brings different perspectives. Our system simulates this "crowd wisdom" through multiple expert personas:
+
+```
+Traditional Crowd:
+  Person 1 → Ideas from perspective 1
+  Person 2 → Ideas from perspective 2
+  Person 3 → Ideas from perspective 3
+  Aggregation → Diverse idea pool
+
+Our "Inner Crowd":
+  LLM + Expert 1 Persona → Ideas from perspective 1
+  LLM + Expert 2 Persona → Ideas from perspective 2
+  LLM + Expert 3 Persona → Ideas from perspective 3
+  Aggregation → Diverse idea pool (simulated crowd)
+```
+
+### Why Multiple Experts Work
+
+1. **Coverage**: Different experts activate different semantic regions
+2. **Redundancy Reduction**: Deduplication removes overlapping ideas
+3. **Diversity by Design**: Expert selection can be optimized for maximum diversity
+4. **Diminishing Returns**: Beyond ~4-6 experts, marginal diversity gains decrease
+
+---
+
+## The Complete Pipeline
+
+### Stage 1: Attribute Decomposition
+
+**Purpose**: Structure the problem space before creative exploration
+
+```
+Input: "Innovative chair design"
+
+Output:
+  Categories: [Material, Function, Usage, User Group]
+
+  Material: [wood, metal, fabric, composite]
+  Function: [support, comfort, mobility, storage]
+  Usage: [office, home, outdoor, medical]
+  User Group: [children, elderly, professionals, athletes]
+```
+
+**Theoretical basis**: Structured decomposition prevents premature fixation on holistic solutions.
+
+### Stage 2: Expert Team Generation
+
+**Purpose**: Assemble diverse perspectives for maximum semantic coverage
+
+```
+Strategies:
+  1. LLM-Generated: Query-specific, prioritizes unconventional experts
+  2. Curated: Pre-selected high-quality occupations
+  3. External Sources: DBpedia, Wikidata for broad coverage
+
+Diversity Optimization:
+  - Domain spread (arts, science, trades, services)
+  - Expertise level variation
+  - Cultural/geographic diversity
+```
+
+### Stage 3: Expert Transformation
+
+**Purpose**: Apply each expert's perspective to each attribute
+
+```
+For each (attribute, expert) pair:
+
+  Input: "Chair comfort" + "Marine Biologist"
+
+  LLM Prompt:
+    "As a marine biologist, how might you reimagine
+     chair comfort using principles from your field?"
+
+  Output: Keywords + Descriptions
+    - "Pressure-distributed seating inspired by deep-sea fish"
+    - "Buoyancy-assisted support reducing pressure points"
+```
+
+### Stage 4: Deduplication
+
+**Purpose**: Ensure idea set is truly diverse, not just numerous
+
+```
+Methods:
+  1. Embedding-based: Fast cosine similarity clustering
+  2. LLM-based: Semantic pairwise comparison (more accurate)
+
+Output:
+  - Unique ideas grouped by similarity
+  - Representative idea selected from each cluster
+  - Diversity metrics computed
+```
+
+### Stage 5: Novelty Validation
+
+**Purpose**: Ground novelty in real-world uniqueness
+
+```
+Process:
+  - Search patent databases for similar concepts
+  - Compute overlap scores
+  - Flag ideas with high existing coverage
+
+Output:
+  - Novelty score per idea
+  - Patent overlap rate for idea set
+```
+
+---
+
+## Testable Hypotheses
+
+### H1: Semantic Diversity
+> Multi-expert generation produces higher semantic diversity than single-expert or direct generation.
+
+**Measurement**: Mean pairwise cosine distance between idea embeddings
+
+### H2: Novelty
+> Ideas from multi-expert generation have lower patent overlap than direct generation.
+
+**Measurement**: Percentage of ideas with existing patent matches
+
+### H3: Expert Count Effect
+> Semantic diversity increases with expert count, with diminishing returns beyond 4-6 experts.
+
+**Measurement**: Diversity vs. expert count curve
+
+### H4: Expert Source Effect
+> LLM-generated experts produce more unconventional ideas than curated/database experts.
+
+**Measurement**: Semantic distance from query centroid
+
+### H5: Fixation Breaking
+> Multi-expert approach produces more ideas outside the top-3 semantic clusters than direct generation.
+
+**Measurement**: Cluster distribution analysis
+
+---
+
+## Expected Contributions
+
+1. **Theoretical**: Formalization of "semantic gravity" as LLM creativity limitation
+2. **Methodological**: Expert-augmented ideation pipeline with evaluation framework
+3. **Empirical**: Quantitative evidence for multi-expert creativity enhancement
+4. **Practical**: Open-source system for innovation ideation
+
+---
+
+## Positioning Against Related Work
+
+| Approach | Limitation | Our Advantage |
+|----------|------------|---------------|
+| Direct LLM generation | Semantic gravity, fixation | Expert-forced semantic jumps |
+| Human brainstorming | Cognitive fatigue, social dynamics | Tireless LLM generation |
+| PersonaFlow (2024) | Research-focused, no attribute structure | Product innovation, structured decomposition |
+| PopBlends (2023) | Two-concept blending only | Multi-expert, multi-attribute blending |
+| BILLY (2025) | Vector fusion less interpretable | Sequential generation, explicit control |