## 🧠 核心能力框架

### 多模態模型生態系譜

#### Vision-Language Models
- **通用 VLM**：GPT-4o/4V, Gemini 1.5/2.0 Pro, Claude 3.5/3.7 Sonnet, Qwen-VL, InternVL, LLaVA 系列
- **文件理解**：LayoutLM, Donut, Nougat, Gemini Document AI, Azure Document Intelligence
- **影片理解**：Gemini Video, Video-LLaMA, InternVideo, Twelve Labs
- **OCR 專精**：PaddleOCR, TrOCR, Google Cloud Vision OCR

#### Audio/Speech Models
- **ASR**：Whisper (large-v3), Deepgram, AssemblyAI, Azure Speech
- **TTS**：ElevenLabs, Azure Neural TTS, OpenAI TTS, CosyVoice
- **Audio Understanding**：Qwen-Audio, Gemini Audio, Wav2Vec 2.0

#### 跨模態基礎設施
- **Embedding**：CLIP, SigLIP, ImageBind, Cohere embed-v3, Voyage multimodal
- **Vector DB**：Pinecone, Weaviate, Qdrant, Milvus, pgvector
- **Model Serving**：vLLM, TGI, TensorRT-LLM, Triton Inference Server, Modal, Replicate
- **Orchestration**：LangChain, LlamaIndex, Semantic Kernel, 自研 DAG orchestrator

### 架構模式庫（Architecture Patterns）

#### Pattern 1: Unified Multimodal Gateway
```
Client → API Gateway → Modality Router → [Vision|Audio|Text] Adapter
         → Fusion Layer → LLM Core → Response Formatter
         → Safety Filter → Client
```
適用：ChatGPT 式通用助手、企業 Copilot

#### Pattern 2: Specialist Ensemble
```
Input → Parallel Specialists (OCR / Object Detection / ASR / NER)
      → Structured Output Aggregator → LLM Reasoning → Action
```
適用：文件處理、合規審查、醫療報告解析

#### Pattern 3: Embedding-First Retrieval
```
Multimodal Input → Encoder(s) → Vector Index
Query (any modality) → Same Encoder → ANN Search → Reranker → LLM Synthesis
```
適用：企業知識庫、電商以圖搜圖、影片內容推薦

#### Pattern 4: Agentic Multimodal Loop
```
User Goal → Planner LLM → Tool Selection [Vision API | Code Exec | Web Search]
         → Observation → Reflection → Next Action → ... → Final Answer
```
適用：複雜任務自動化、research agent、coding agent with screenshot

### Evaluation 框架

| 維度 | 指標 | 工具/方法 |
|------|------|-----------|
| 準確性 | Accuracy, F1, CIDEr, BLEU | 自建 golden set + human eval |
| 幻覺率 | Hallucination rate, faithfulness | RAGAS, DeepEval, 人工審計 |
| 延遲 | P50/P95/P99 latency | Locust, k6, 自建 benchmark |
| 成本 | $/1K requests, $/1M tokens | Cloud billing + custom dashboard |
| 安全 | Toxicity, PII leakage, jailbreak success | LlamaGuard, 紅隊測試 |
| 穩健性 | OOD performance, adversarial robustness | 壓力測試集、corner case suite |

### 決策矩陣模板

評估任何多模態方案時，使用 **SCORE 框架**：
- **S**calability：能否支撐 10x 流量增長？
- **C**ost：3 年 TCO 是否可接受？
- **O**bservability：能否在 5 分鐘內定位 root cause？
- **R**isk：合規、安全、vendor lock-in 風險？
- **E**volution：6 個月後模型升級的路徑是否清晰？

### 團隊建設 Blueprint

| 角色 | 核心技能 | 團隊比例（參考） |
|------|----------|------------------|
| ML Engineer (Multimodal) | VLM fine-tuning, data pipeline | 30% |
| Backend/Infra Engineer | GPU scheduling, serving optimization | 25% |
| Data Engineer | ETL, labeling pipeline, quality control | 20% |
| ML Research Scientist | Novel architecture, eval methodology | 15% |
| MLOps / Platform Engineer | CI/CD, monitoring, cost optimization | 10% |

### 持續學習來源
- arXiv (cs.CV, cs.CL, cs.MM)
- Papers With Code leaderboards
- LMSYS Chatbot Arena (vision arena)
- Hugging Face model cards & Open LLM Leaderboard
- MLCommons MLPerf inference benchmarks