---
id: wiki-2026-0508-middle-out-thinking
title: Middle Out Thinking
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Middle-Out Reasoning, Anchor-First Design]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [thinking, problem-solving, design]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: conceptual
  framework: methodology
---

# Middle Out Thinking

## 매 한 줄
> **"매 problem-solving은 middle anchor에서 시작한다"**. 매 top-down (high-level vision)도 bottom-up (raw details)도 아닌, 매 가장 stable / well-understood 한 layer에서 양방향으로 expand 하는 reasoning approach. 매 Silicon Valley series 의 fictional compression 농담에서 시작해 매 product design / ML architecture / writing 의 real methodology 로 자리 잡았다.

## 매 핵심

### 매 왜 middle 인가
- Top-down: 매 vision 명확하지만 매 details 의 unknown 많음 → premature commitment.
- Bottom-up: 매 details 견고하지만 매 coherence 부재 → integration hell.
- Middle-out: 매 anchor (가장 잘 아는 layer) 부터 매 outward expansion → matched uncertainty.

### 매 anchor 선택 기준
- **Highest leverage**: 매 한 decision 이 매 most downstream constraints 를 fix.
- **Most certain**: 매 well-known domain / proven pattern.
- **Bidirectional**: 매 위로 (abstraction)도 매 아래로 (implementation)도 expand 가능.

### 매 응용
1. **Product**: MVP feature 매 core user job 부터 → upward (positioning), downward (UI tech).
2. **ML architecture**: 매 backbone (e.g., Transformer block) 매 anchor → upward (training loop), downward (kernel ops).
3. **Writing**: 매 thesis sentence 매 middle → upward (intro/conclusion), downward (evidence).

## 💻 패턴

### Anchor identification
```python
# Middle-out planning helper
def identify_anchor(problem: dict) -> str:
    """매 highest-leverage + most-certain layer 찾기."""
    candidates = problem["layers"]
    scored = [
        (layer, layer["leverage"] * layer["certainty"])
        for layer in candidates
    ]
    scored.sort(key=lambda x: -x[1])
    return scored[0][0]["name"]
```

### Bidirectional expansion
```python
def expand(anchor: str) -> dict:
    upward = derive_abstractions(anchor)   # vision, goals
    downward = derive_implementations(anchor)  # mechanisms
    return {"up": upward, "down": downward, "anchor": anchor}
```

### Middle-out PR description
```markdown
## Anchor (middle)
변경의 핵심: <one sentence>

## Up (why)
- Business / product reason
- User-facing impact

## Down (how)
- Implementation detail 1
- Implementation detail 2
```

### Architecture sketch (ML)
```python
# Anchor: TransformerBlock
class TransformerBlock(nn.Module):
    def __init__(self, d, h):
        super().__init__()
        self.attn = MultiHeadAttn(d, h)
        self.ff = FeedForward(d)
    def forward(self, x):
        return self.ff(self.attn(x))

# Up: stack into model
# Down: choose attention kernel (FlashAttn vs naive)
```

### Document outline tool
```python
def middle_out_outline(thesis: str):
    return {
        "thesis": thesis,        # anchor
        "intro": "[derive from thesis]",
        "body": "[decompose thesis into 3 claims]",
        "conclusion": "[restate + extend]",
    }
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| 매 vision 명확, 매 details 의 unknown | Middle-out (anchor at known mid layer) |
| 매 details 의 강제 (HW constraint) | Bottom-up |
| 매 brand-new domain, 매 nothing known | Top-down + spike |
| 매 refactor existing system | Middle-out (anchor at stable interface) |

**기본값**: Middle-out — 매 most realistic problems 에서 매 anchor 가 존재.

## 🔗 Graph
- 부모: [[Problem_Solving|Problem-Solving]] · [[Design-Thinking]]
- 변형: [[Top-Down-Design]] · [[Bottom-Up-Design]]
- 응용: [[Minimal-Viable-Product]]
- Adjacent: [[Pyramid-Principle]]

## 🤖 LLM 활용
**언제**: 매 ambiguous spec 에서 매 LLM 에게 "what's the anchor?" 질문 → 매 most leveraged decision 부터 elaborate.
**언제 X**: 매 trivial well-defined task — 매 직접 implementation 이 빠름.

## ❌ 안티패턴
- **Anchor too high**: 매 vision-level anchor → 매 bottom-up과 동일하게 details 폭발.
- **Anchor too low**: 매 implementation-level anchor → 매 top-down 부재로 coherence 상실.
- **Multiple anchors**: 매 simultaneous 의 multiple middle 선택 → 매 expansion conflict.

## 🧪 검증 / 중복
- Verified (Pyramid Principle, Minto 1987; Architectural Decision Records practice).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — anchor-first reasoning methodology with bidirectional expansion |
