deep-dive

# Deep Dive: Concept + Paper Integration

Learn domain concepts while tracing their research origins. Combines **domain-vocab** (conceptual vocabulary) with **paper-flow** (citation genealogy) for contextualized understanding.

## When to Use

- "Deep dive into [domain]"
- "Explore [domain] deeply"
- "Domain exploration"
- "I want to learn [domain] concepts and their papers together"
- Learning a new field with academic depth

## When NOT to Use

- Quick concept lookup → use `domain-vocab`
- Single concept history → use `trace`
- Latest research only → use `frontier`
- Citation tree only → use `paper-flow`

## Core Value

> Learning concepts alone tells you "what". Learning with paper genealogy tells you "why, where, and how it evolved".

## Workflow

### Phase 1: Domain Entry (domain-vocab)

**Objective:** Extract core concepts with authentic expert tokens

**Actions:**
1. Invoke domain-vocab Phase 0-2:
   - Token priming (key figures, literature sampling)
   - Domain identification (scope, depth level)
   - Core concept extraction (20-30 terms)

2. Identify **anchor concepts** (5-8):
   - High-impact terms that shaped the field
   - Concepts with clear research origins
   - Terms that connect to seminal papers

**Output:**
```yaml
domain: "[identified domain]"
depth_level: L2  # Start at practitioner level
concepts:
  - name: "[concept]"
    difficulty: entry|intermediate|advanced
    is_anchor: true|false  # Anchors get paper-flow treatment
    potential_papers: ["keyword hints for paper search"]
```

**User Checkpoint:**
> "I'll explore the research lineage for these anchor concepts: [anchors]. Proceed?"

### Phase 2: Research Genealogy (paper-flow)

**Objective:** Build citation trees for anchor concepts

**Actions:**
1. For each anchor concept:
   - Search papers using concept name + domain context
   - Identify **root paper** (seminal work that introduced/defined the concept)
   - Build citation tree (parents + children)
   - Extract key papers (high-citation