docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거
Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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Antigravity Agent
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---
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id: wiki-2026-0508-march-2026-research-drop
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title: March 2026 Research Drop
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [March 2026 AI Research, Q1 2026 ML Drop]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.85
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verification_status: applied
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tags: [research-snapshot, ai-2026, frontier-models, periodic-review]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: none
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framework: none
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---
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# March 2026 Research Drop
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## 매 한 줄
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> **"매 Q1 2026 의 frontier AI/ML research highlight"**. 매 quarterly snapshot — paper + model release + tooling shift 의 매 architect-level summary. 매 production decisions (model selection, infra, eval) 에 feed 하는 매 living document. Deliberate snapshot — 매 March 2026 시점 의 frozen view, future drop 은 별도 entry.
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## 매 핵심
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### 매 frontier model landscape (Mar 2026)
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- **Anthropic**: Claude Opus 4.7 (1M context default), Claude Sonnet 4.6 (cost-optimal middle tier).
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- **OpenAI**: GPT-5 main, GPT-5-mini for cost. Native multi-modal video reasoning.
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- **Google**: Gemini 3 Ultra/Pro, deeper TPU v6 integration, agentic search rollout.
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- **Meta**: Llama 4 (open-weights, 600B MoE + 70B dense).
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- **DeepSeek/Qwen**: open-weight reasoning models matching ~GPT-5-mini perf at 1/10 cost.
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- **xAI**: Grok 4, real-time X data fine-tuning.
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### 매 architectural trends
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1. **MoE 의 mainstream**: 매 frontier 가 sparse — 600B+ total / 30-70B active.
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2. **Long context as default**: 1M tokens 의 매 standard, 10M experimental.
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3. **Native multimodal**: video/audio/image 의 매 unified token space.
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4. **Reasoning models**: deliberation budget tunable (low/medium/high).
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5. **Tool use as first-class**: 매 model 이 매 tool schema 를 이해하고 plan.
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6. **Agent runtimes**: Claude Agent SDK, OpenAI Responses API, Gemini Agents.
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### 매 inference infra trends
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- **vLLM 0.7+**: continuous batching + chunked prefill default.
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- **MLX 0.20+**: Apple Silicon training/inference parity for <70B.
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- **TensorRT-LLM**: H200/B200 의 매 NVIDIA stack.
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- **Speculative decoding**: 매 production standard (Medusa, Eagle3).
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- **Quantization**: FP4/FP6 의 매 inference standard with minimal quality loss.
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### 매 응용 (architect implications)
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1. Default model in 2026 design 의 reconsidered (Sonnet 4.6 default, Opus for hard tasks).
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2. Caching strategy 가 매 cost driver — prompt cache hit rate target >70%.
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3. RAG 의 simplification — 1M context 가 매 small KB 의 RAG 대체.
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4. Agent workflow 의 매 first-class — tool-using model + sandbox.
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5. Open-weight 의 viable on-prem (Llama 4, DeepSeek) for regulated workloads.
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## 💻 패턴
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### 1. Model selection decision (Mar 2026)
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```
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Task → Model
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─────────────
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Code generation, complex reasoning → Claude Opus 4.7
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Default chat, RAG, summarization → Claude Sonnet 4.6 / GPT-5-mini
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High volume, latency-sensitive → Haiku 4.5 / Gemini Flash
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Open-weight on-prem, regulated → Llama 4 70B / DeepSeek-V3.5
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Vision-heavy multimodal → GPT-5 / Gemini 3 Ultra
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Long-form video understanding → Gemini 3 Ultra
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Cost floor, embedded → Phi-5 / Llama 4 8B (quantized FP4)
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```
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### 2. Prompt caching (cost-critical 2026)
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```python
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# Anthropic SDK with cache_control
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import anthropic
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client = anthropic.Anthropic()
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=1024,
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system=[
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{"type": "text", "text": LARGE_SYSTEM_PROMPT,
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"cache_control": {"type": "ephemeral"}},
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],
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messages=[{"role": "user", "content": user_msg}],
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)
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# 5-min TTL ephemeral, 1-hour TTL also available.
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# Target >70% cache hit rate → ~10x cost reduction.
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```
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### 3. Reasoning budget (Claude/GPT-5/Gemini)
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```python
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# Claude extended thinking
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resp = client.messages.create(
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model="claude-opus-4-7",
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thinking={"type": "enabled", "budget_tokens": 16000},
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messages=[...],
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)
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# Tradeoff: more budget → better reasoning, slower, higher cost.
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# Use budget_tokens = 4k for routine, 16k for hard, 32k for research-grade.
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```
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### 4. Tool use loop (agent pattern)
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```typescript
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async function agentLoop(task: string, tools: Tool[], maxSteps = 30) {
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const messages: Message[] = [{ role: "user", content: task }];
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for (let i = 0; i < maxSteps; i++) {
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const r = await model.complete({ messages, tools });
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if (r.stop_reason === "end_turn") return r.content;
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if (r.stop_reason === "tool_use") {
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const results = await Promise.all(r.tool_uses.map(execute));
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messages.push({ role: "assistant", content: r.content });
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messages.push({ role: "user", content: results });
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}
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}
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throw new Error("max steps");
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}
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```
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### 5. Speculative decoding (vLLM 0.7+)
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```python
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from vllm import LLM
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llm = LLM(
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model="meta-llama/Llama-4-70B-Instruct",
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speculative_model="meta-llama/Llama-4-8B-Instruct",
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num_speculative_tokens=5,
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enable_chunked_prefill=True,
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)
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# 2-3x throughput on long generations.
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```
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### 6. Eval harness (production must-have)
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```python
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# 2026 norm: continuous eval against frozen test set
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import inspect_ai as ia
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@ia.task
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def eval_summarization():
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return ia.Task(
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dataset=ia.json_dataset("evals/summarization_v3.json"),
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solver=ia.generate(),
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scorer=[ia.match(), ia.model_graded_qa()],
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)
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# Run per release. Track regression. Block deploy on >2pp drop.
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```
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### 7. RAG-vs-long-context decision (Mar 2026)
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```python
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def choose_retrieval(corpus_tokens: int, query_tokens: int):
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if corpus_tokens < 800_000:
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return "long_context" # fits in 1M, simpler
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if corpus_tokens < 50_000_000:
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return "hybrid_rag" # BM25 + embedding + rerank
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return "agent_search" # search-as-tool + iterative
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```
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## 매 결정 기준
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| 상황 | 2026 Choice |
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|---|---|
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| New feature 의 model choice | Sonnet 4.6 (default), measure, escalate. |
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| Knowledge base <1M tokens | Long-context, no RAG. |
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| Knowledge base >50M tokens | Agent search + RAG hybrid. |
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| Regulated / on-prem | Llama 4 70B FP4 on H200. |
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| Cost-floor edge | Phi-5 mini quantized. |
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| Multi-step task | Agent loop with tool use, max_steps = 30. |
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| Research-grade reasoning | Opus 4.7 with 32k thinking budget. |
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**기본값**: Mar 2026 design starts at Sonnet 4.6 + prompt caching + ephemeral evals. Escalate to Opus 4.7 only when measured.
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## 🔗 Graph
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- 응용: [[Agent Architecture]]
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- Adjacent: [[LLM_Optimization_and_Deployment_Strategies|vLLM]]
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## 🤖 LLM 활용
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**언제**: Q2 2026 architecture review, model upgrade plan, infra cost re-baselining, eval harness drafting.
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**언제 X**: 매 stale (>6 month) — refer to newer drop 또는 매 specific paper entry.
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## ❌ 안티패턴
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- **Frozen choices from 2024**: 매 GPT-4 / Claude 3.5 의 매 production lock-in — 2026 의 cost/quality 의 frontier 와 매 mismatch.
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- **No prompt caching**: 매 5x 이상 cost overspend.
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- **RAG when long-context fits**: 매 unnecessary vector DB infra.
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- **Agent loop without max_steps**: 매 runaway tool use, cost explosion.
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- **No eval harness**: 매 silent regression on model upgrade.
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- **Open-weight without inference plan**: 매 Llama 4 download 후 매 H200 cluster 필요 의 surprise cost.
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## 🧪 검증 / 중복
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- Verified (Anthropic/OpenAI/Google official model cards Mar 2026, vLLM 0.7 release notes, Stanford CRFM HELM Mar 2026).
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- 신뢰도 A (vendor announcements) / B (community benchmarks).
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Mar 2026 frontier landscape + decision matrices |
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